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89 </style></head><body><p class="Standard"><br />
90 </p><p class="P1">Documentation Iramuteq</p><p class="P2"> </p><p class="P2">version 0.4 alpha 1</p><p class="P1"> </p><p class="P1">Manuel utilisateur</p><p class="P2"> </p><p class="P2"> </p><p class="P2">Pierre Ratinaud</p><p class="P2"> </p><p class="P2">Licence GNU FDL</p><p class="Standard"> <br />
92 <p class="Standard"><br />
95 <!--mozToc h1 1 h2 2 h3 3 h4 4 h5 5 h6 6-->
97 <a href="#mozTocId659522"> 1 Présentation d'iramuteq</a><br />
98 <div style="margin-left: 40px;"><a href="#mozTocId337076"> 1.1 R</a><br />
100 <div style="margin-left: 40px;"><a href="#mozTocId472701"> 1.2 Python</a><br />
101 <a href="#mozTocId580887"> 1.3 Lexique 3</a><br />
103 <a href="#mozTocId505681"> 2 Analyses de textes</a><br />
104 <div style="margin-left: 40px;"><a href="#mozTocId913750"> 2.1 Format des données en entrée</a><br />
105 <a href="#mozTocId308077"> 2.2 Ouverture d'un fichier texte</a><br />
106 <a href="#mozTocId450334"> 2.3 Traitements commun aux analyses</a><br />
108 <div style="margin-left: 80px;"><a href="#mozTocId324766"> 2.3.1 Nettoyage 1</a><br />
109 <a href="#mozTocId718989"> 2.3.2 Dictionnaire des expressions</a><br />
110 <a href="#mozTocId531224"> 2.3.3 Nettoyage 2</a><br />
111 <a href="#mozTocId952934"> 2.3.4 Lemmatisation</a><br />
113 <div style="margin-left: 40px;"><a href="#mozTocId367717"> 2.4 Statistiques textuelles</a><br />
115 <div style="margin-left: 80px;"><a href="#mozTocId302319"> 2.4.1 Description</a><br />
116 <a href="#mozTocId29910"> 2.4.2 Résultats</a><br />
117 <a href="#mozTocId867660"> 2.4.3 Fichiers en sortie</a><br />
119 <div style="margin-left: 40px;"><a href="#mozTocId419322"> 2.5 Comme Lexico</a><br />
121 <div style="margin-left: 80px;"><a href="#mozTocId695160"> 2.5.1 Options</a><br />
122 <a href="#mozTocId763197"> 2.5.2 Résultats</a><br />
124 <div style="margin-left: 40px;"><a href="#mozTocId723694"> 2.6 AFC sur UCI</a><br />
126 <div style="margin-left: 80px;"><a href="#mozTocId115880"> 2.6.1 Description</a><br />
127 <a href="#mozTocId425577"> 2.6.2 Options</a><br />
128 <a href="#mozTocId16996"> 2.6.3 Résultats</a><br />
130 <div style="margin-left: 40px;"><a href="#mozTocId285818"> 2.7 Classification</a><br />
132 <div style="margin-left: 80px;"><a href="#mozTocId447946"> 2.7.1 Méthode ALCESTE</a><br />
134 <div style="margin-left: 120px;"><a href="#mozTocId767561"> 2.7.1.1 Description</a><br />
135 <a href="#mozTocId668017"> 2.7.1.2 Options</a><br />
136 <a href="#mozTocId418081"> 2.7.1.3 Résultats</a><br />
138 <div style="margin-left: 160px;"><a href="#mozTocId373356"> 2.7.1.3.1 Options des profils</a><br />
139 <a href="#mozTocId886605"> 2.7.1.4 Fichiers en sortie</a><br />
141 <div style="margin-left: 80px;"><a href="#mozTocId82973"> 2.7.2 Par matrice des distances</a><br />
143 <div style="margin-left: 120px;"><a href="#mozTocId911832"> 2.7.2.1 Description</a><br />
144 <a href="#mozTocId470612"> 2.7.2.2 Options</a><br />
145 <a href="#mozTocId556046"> 2.7.2.3 Résultats</a><br />
147 <a href="#mozTocId112924"> 3 Analyses de tableaux de données</a><br />
148 <div style="margin-left: 40px;"><a href="#mozTocId830044"> 3.1 Format des données en entrée</a><br />
149 <a href="#mozTocId473852"> 3.2 Fréquences</a><br />
150 <a href="#mozTocId287592"> 3.3 Chi 2</a><br />
151 <a href="#mozTocId881862"> 3.4 Classification</a><br />
153 <div style="margin-left: 80px;"><a href="#mozTocId541874"> 3.4.1 Méthode ALCESTE</a><br />
154 <a href="#mozTocId348003"> 3.4.2 Par matrice des distances</a><br />
156 <div style="margin-left: 40px;"><a href="#mozTocId44642"> 3.5 AFCM</a><br />
157 <a href="#mozTocId714196"> 3.6 Graphes</a><br />
159 <a href="#mozTocId899808"> 4 Bibliographie</a><br />
160 <a href="#mozTocId286341"> 5 Annexes</a>
161 <p class="Standard"><br />
163 <h1 class="P18"><a class="mozTocH1" name="mozTocId659522" /><a id="a__1__Présentation_d'iramuteq"><span style="margin-right: 0.381cm;"> 1 </span></a>Présentation d'iramuteq</h1><p class="Standard"> </p><p class="Standard">Iramuteq est un logiciel d'analyse de textes et de tableaux de données. Il s'appuie sur le logiciel de statistique R (<a href="http://www.r-project.org/">http://www.r-project.org</a>), sur le langage python (<a href="http://www.python.org/">http://www.python.org</a>) et sur la base de données lexicales Lexique (<a href="http://www.lexique.org/">http://www.lexique.org</a>).</p><p class="Standard"> </p><p class="Standard"> </p><p class="P7">ATTENTION</p><p class="P7"> </p><p class="warning">Iramuteq est en cours de développement. Regardez les informations disponibles sur la page <a href="http://repere.no-ip.org/logiciel/iramuteq">http://repere.no-ip.org/logiciel/iramuteq</a> pour connaître la fiabilité des différentes analyses.</p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId337076" /><a id="a__1_1__R"><span style="margin-right: 0.381cm;"> 1.1 </span></a>R</h2><p class="Standard"> </p><p class="Standard"><a href="http://www.r-project.org/">http://www.r-project.org</a></p><p class="Standard"> </p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId472701" /><a id="a__1_2__Python"><span style="margin-right: 0.381cm;"> 1.2 </span></a>Python</h2><p class="Standard"> </p><p class="Standard"><a href="http://www.python.org/">http://www.python.org</a></p><p class="Standard"> </p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId580887" /><a id="a__1_3__Lexique_3"><span style="margin-right: 0.381cm;"> 1.3 </span></a>Lexique 3</h2><p class="Standard"> </p><p class="Standard"><a href="http://www.lexique.org/">http://www.lexique.org</a></p><p class="Standard"> </p><h1 class="Heading_20_1"><a class="mozTocH1" name="mozTocId505681" /><a id="a__2__Analyses_de_textes"><span style="margin-right: 0.381cm;"> 2 </span></a>Analyses de textes</h1><p class="Standard"> </p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId913750" /><a id="a__2_1__Format_des_données_en_entrée"><span style="margin-right: 0.381cm;"> 2.1 </span></a>Format des données en entrée</h2><p class="Standard"> </p><p class="Text_20_body">Les fichiers d'entrée doivent être au format texte brut (.txt) et respecter les règles de formatage des corpus ALCESTE. </p><p class="Text_20_body">Dans
164 ce formatage, l'unité de base est appelée « unité de contexte
165 initiale » (UCI). Une UCI peu représenter un entretien, un
166 article, un livre ou tout autre type de documents. Un corpus peut
167 contenir une ou plusieurs UCI (mais au minimum une). </p><p class="Text_20_body"><br />Les UCI sont introduites par quatre étoiles (****) suivies d'une série de variables étoilées séparées par un espace.</p><div class="note"><div style="padding: 0pt; height: 0.534cm; width: 0.496cm; float: left; position: relative; left: -0.499cm;" class="fr1" id="images1"><img style="height: 0.534cm; width: 0.496cm;" alt="" src="data:image/*;base64,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" /></div><div style="position: relative; left: -0.499cm;"> Une uci doit obligatoirement avoir au moins une variable étoilée</div></div><div style="margin: 0pt; padding: 0pt; clear: both; line-height: 0pt; width: 0pt; height: 0pt;"> </div><p class="Text_20_body">Il
168 est possible de placer des variables étoilées à l'intérieur des corpus
169 en les introduisant en début de ligne par un tiret et une étoile (-*).
170 La ligne ne doit contenir que cette variable. </p><p class="Text_20_body">Il
171 est possible d'introduire dans le corps du texte des formes qui seront
172 traitées comme des variables étoilées. Il faut alors que ces formes
173 commencent et se terminent par un _. :</p><p class="Text_20_body">Exemple</p><p class="exemple">texte texte _rire_ texte texte texte</p><p class="Text_20_body"> </p><p class="Text_20_body">Le texte contient, de préférence, les caractères de ponctuations.</p><p class="Standard"> </p><p class="Text_20_body">Exemple d'un corpus sans thématique :</p><p class="exemple_borderStart">**** *var1_1 *var2_2</p><p class="exemple">texte
174 texte texte texte texte texte texte texte texte texte texte texte texte
175 texte texte texte texte texte texte texte texte texte texte texte
176 texte texte texte texte texte texte texte texte texte texte texte
177 texte texte texte texte texte texte texte texte texte texte texte texte
178 texte texte texte texte texte texte texte texte texte texte texte
179 texte texte texte texte texte texte texte texte texte texte texte
180 texte texte texte texte texte texte texte texte texte texte texte
181 texte texte texte texte texte texte texte texte texte texte texte texte
182 texte texte texte texte texte texte texte texte texte texte texte
183 texte texte texte texte texte texte texte </p><p class="exemple" /><p class="exemple">**** *var1_2 *var2_3</p><p class="exemple_borderEnd">texte
184 texte texte texte texte texte texte texte texte texte texte texte texte
185 texte texte texte texte texte texte texte texte texte texte texte
186 texte texte texte texte texte texte texte texte texte texte texte
187 texte texte texte texte texte texte texte texte texte texte texte texte
188 texte texte texte texte texte texte texte texte texte texte texte
189 texte texte texte texte texte texte texte texte texte texte texte
190 texte texte texte texte texte texte texte texte texte texte texte
191 texte texte texte texte texte texte texte texte texte texte texte texte
192 texte texte texte texte texte texte texte </p><p class="Text_20_body"> </p><p class="Text_20_body">Exemple d'un corpus avec thématique : </p><p class="exemple_borderStart">**** *var1_1 *var2_2</p><p class="exemple" /><p class="exemple">-*thematique1</p><p class="exemple">texte
193 texte texte texte texte texte texte texte texte texte texte texte texte
194 texte texte texte texte texte texte texte texte texte texte texte texte
195 texte texte texte texte texte texte texte texte texte texte texte texte
196 texte texte texte texte texte texte texte texte texte texte texte texte
197 texte texte texte texte texte texte texte texte texte texte texte texte
198 texte</p><p class="exemple" /><p class="exemple">-*thematique2</p><p class="exemple">texte
199 texte texte texte texte texte texte texte texte texte texte texte texte
200 texte texte texte texte texte texte texte texte texte texte texte texte
201 texte texte texte texte texte texte texte texte texte texte texte texte
202 texte texte texte texte texte texte texte texte texte texte texte texte
203 texte texte texte texte texte texte texte texte texte texte texte texte
204 texte</p><p class="exemple" /><p class="exemple">**** *var1_2 *var2_3</p><p class="exemple" /><p class="exemple">-*thematique1</p><p class="exemple">texte
205 texte texte texte texte texte texte texte texte texte texte texte texte
206 texte texte texte texte texte texte texte texte texte texte texte texte
207 texte texte texte texte texte texte texte texte texte texte texte texte
208 texte texte texte texte texte texte texte texte texte texte texte texte
209 texte texte texte texte texte texte texte texte texte texte texte texte
210 texte</p><p class="exemple" /><p class="exemple">-*thematique2</p><p class="exemple_borderEnd">texte
211 texte texte texte texte texte texte texte texte texte texte texte texte
212 texte texte texte texte texte texte texte texte texte texte texte texte
213 texte texte texte texte texte texte texte texte texte texte texte texte
214 texte texte texte texte texte texte texte texte texte texte texte texte
215 texte texte texte texte texte texte texte texte texte texte texte texte
216 texte</p><p class="Standard"> </p><p class="Standard"> </p><!--Next 'div' was a 'text:p'.--><div class="note"> <!--Next 'div' is a draw:frame.--><div style="padding: 0pt; height: 0.534cm; width: 0.496cm; float: left; position: relative; left: -0.499cm;" class="fr1" id="images2"><img style="height: 0.534cm; width: 0.496cm;" alt="" src="data:image/*;base64,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" /></div><!--Next 'div' added for floating.--><div style="position: relative; left: -0.499cm;">Dans
217 un corpus avec thématique, tous les paragraphes d'une UCI doivent
218 appartenir à une thématique. La construction suivante n'est donc pas
219 possible :</div></div><div style="margin: 0pt; padding: 0pt; clear: both; line-height: 0pt; width: 0pt; height: 0pt;"> </div><p class="note">**** *var1_1</p><p class="note">texte texte texte texte texte </p><p class="note">-*thematique1</p><p class="note_borderEnd">texte texte texte texte texte texte </p><p class="Standard"> </p><p class="Standard"> </p><!--Next 'div' was a 'text:p'.--><div class="note"> <!--Next 'div' is a draw:frame.--><div style="padding: 0pt; height: 0.534cm; width: 0.496cm; float: left; position: relative; left: -0.499cm;" class="fr1" id="Image2"><img style="height: 0.534cm; width: 0.496cm;" alt="" src="data:image/*;base64,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" /></div><!--Next 'div' added for floating.--><div style="position: relative; left: -0.499cm;">
220 Les variables étoilées et les thématiques introduites dans le corpus ne
221 doivent pas contenir d'espaces ou de caractères spéciaux. Elles ne
222 doivent contenir que des caractères parmi a-z, A-Z, 1-9 et des tirets
223 bas (_).</div></div><div style="margin: 0pt; padding: 0pt; clear: both; line-height: 0pt; width: 0pt; height: 0pt;"> </div><p class="note">*age 18 ans n'est pas un bon codage</p><p class="note">*age_18 est un bon codage</p><p class="note">*entretien_d'Emilie n'est pas un bon codage</p><p class="note_borderEnd">*ent_emilie est un bon codage</p><p class="Standard"> </p><p class="Standard"> </p><!--Next 'div' was a 'text:p'.--><div class="note"> <!--Next 'div' is a draw:frame.--><div style="padding: 0pt; height: 0.534cm; width: 0.496cm; float: left; position: relative; left: -0.499cm;" class="fr1" id="images3"><img style="height: 0.534cm; width: 0.496cm;" alt="" src="data:image/*;base64,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" /></div><!--Next 'div' added for floating.--><div style="position: relative; left: -0.499cm;">Les
224 codages de la forme *variable_modalité doivent être privilégiés pour
225 les variables illustratives. Ils permettent des analyses
226 complémentaires. </div></div><div style="margin: 0pt; padding: 0pt; clear: both; line-height: 0pt; width: 0pt; height: 0pt;"> </div><p class="note_borderEnd">Exemple : *sex_h pour les hommes et *sex_f pour les femmes permet de repérer la variable sex et les modalités h et f.</p><p class="Standard"> </p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId308077" /><a id="a__2_2__Ouverture_d'un_fichier_texte"><span style="margin-right: 0.381cm;"> 2.2 </span></a>Ouverture d'un fichier texte</h2><p class="Text_20_body">Fichier → Ouvrir un corpus texte</p><p class="Text_20_body">Vous devez préciser l'encodage du fichier et la langue du corpus.</p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId450334" /><a id="a__2_3__Traitements_commun_aux_analyses"><span style="margin-right: 0.381cm;"> 2.3 </span></a>Traitements commun aux analyses</h2><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId324766" /><a id="a__2_3_1__Nettoyage_1"><span style="margin-right: 0.381cm;"> 2.3.1 </span></a>Nettoyage 1</h3><p class="Text_20_body">Les
227 corpus texte sont passés en minuscules. Tous les caractères qui ne sont
228 pas dans la liste des caractères retenus sont remplacés par des
229 espaces. Toutes les successions d'espaces ou de sauts de ligne sont
230 remplacés par un espace ou un saut de ligne. Les apostrophes (’) sont
231 remplacées par des apostrophes (').</p><p class="Text_20_body">Caractères retenus : a-zA-Z0-9àÀâÂäÄáÁéÉèÈêÊëËìÌîÎïÏòÒôÔöÖùÙûÛüÜçÇ’ñ.:,;!?\n*'_-</p><p class="note">Cette liste devrait devenir paramétrable</p><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId718989" /><a id="a__2_3_2__Dictionnaire_des_expressions"><span style="margin-right: 0.381cm;"> 2.3.2 </span></a>Dictionnaire des expressions</h3><p class="Text_20_body">Le
232 dictionnaire des expressions contient des expressions ou des mots
233 contenant des tirets (-) des apostrophes (') ou des espaces. Il permet
234 de traiter ces expressions comme un tout. Par exemple, le mot
235 aujourd'hui sera traité comme la forme aujourd_hui. L'expression
236 « vis-à-vis » sera transformée en « vis_à_vis ». Le
237 dictionnaire des expressions est disponible dans le répertoire
238 d'installation d'iramuteq, dans le sous-répertoire
239 « dictionnaire ».</p><p class="Text_20_body">L'utilisation de ce dictionnaire est optionnel.</p><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId531224" /><a id="a__2_3_3__Nettoyage_2"><span style="margin-right: 0.381cm;"> 2.3.3 </span></a>Nettoyage 2</h3><p class="Text_20_body">Les apostrophes (') et les tirets (-) sont remplacés par des espaces.</p><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId952934" /><a id="a__2_3_4__Lemmatisation"><span style="margin-right: 0.381cm;"> 2.3.4 </span></a>Lemmatisation</h3><p class="Text_20_body">Les verbes sont réduits à l'infinitif, les noms et les adjectifs sont réduits au masculin singulier.</p><p class="Text_20_body">Exemple :</p><p class="exemple_borderStart">mangé, mangeons, mangera → manger</p><p class="exemple_borderEnd">professionnelles, professionnelle, professionnels, professionnel → professionnel</p><p class="Text_20_body"> </p><p class="Text_20_body">La lemmatisation est optionnelle.</p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId367717" /><a id="a__2_4__Statistiques_textuelles"><span style="margin-right: 0.381cm;"> 2.4 </span></a>Statistiques textuelles</h2><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId302319" /><a id="a__2_4_1__Description"><span style="margin-right: 0.381cm;"> 2.4.1 </span></a>Description</h3><p class="Standard">Analyse de texte → Statistiques textuelles</p><p class="Standard">Cette
240 analyse propose des statistiques simples sur les corpus texte :
241 effectifs de toutes les formes, effectifs des formes actives et
242 supplémentaires, liste des hapax.</p><p class="Standard"> </p><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId29910" /><a id="a__2_4_2__Résultats"><span style="margin-right: 0.381cm;"> 2.4.2 </span></a>Résultats</h3><p class="Standard">Les
243 résultats se présentent sous forme de listes. Un clique droit sur une
244 forme permet d'accéder aux formes associées et à un concordancier.</p><p class="Standard"> </p><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId867660" /><a id="a__2_4_3__Fichiers_en_sortie"><span style="margin-right: 0.381cm;"> 2.4.3 </span></a>Fichiers en sortie</h3><p class="Standard"> </p><table class="Tableau1" border="0" cellpadding="0" cellspacing="0"><colgroup><col width="371" /><col width="371" /></colgroup><tbody><tr><td style="text-align: left; width: 8.498cm;" class="Tableau1_A1"><p class="Standard">Répertoire de sortie</p></td><td style="text-align: left; width: 8.5cm;" class="Tableau1_B1"><p class="Standard">NomDuCorpus_Stat_x</p></td></tr><tr><td colspan="2" style="text-align: left; width: 8.498cm;" class="Tableau1_A2"><p class="Table_20_Contents">Fichiers en sortie :</p></td></tr><tr><td style="text-align: left; width: 8.498cm;" class="Tableau1_A3"><p class="Table_20_Contents">total.csv</p></td><td style="text-align: left; width: 8.5cm;" class="Tableau1_A2"><p class="Table_20_Contents">Toute les formes et leurs effectifs</p></td></tr><tr><td style="text-align: left; width: 8.498cm;" class="Tableau1_A3"><p class="Table_20_Contents">formes_supplémentaires.csv</p></td><td style="text-align: left; width: 8.5cm;" class="Tableau1_A2"><p class="Table_20_Contents">Les formes supplémentaires et leurs effectifs</p></td></tr><tr><td style="text-align: left; width: 8.498cm;" class="Tableau1_A3"><p class="Table_20_Contents">formes_actives.csv</p></td><td style="text-align: left; width: 8.5cm;" class="Tableau1_A2"><p class="Table_20_Contents">Les formes actives et leurs effectifs</p></td></tr><tr><td style="text-align: left; width: 8.498cm;" class="Tableau1_A3"><p class="Table_20_Contents">hapax.csv</p></td><td style="text-align: left; width: 8.5cm;" class="Tableau1_A2"><p class="Table_20_Contents">Les hapax</p></td></tr></tbody></table><p class="Standard"> </p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId419322" /><a id="a__2_5__Comme_Lexico"><span style="margin-right: 0.381cm;"> 2.5 </span></a>Comme Lexico</h2><p class="Standard">Analyse de texte → Comme lexico</p><p class="Standard">Reproduit une des analyses du logiciel Lexico (<a href="http://www.tal.univ-paris3.fr/lexico/">http://www.tal.univ-paris3.fr/lexico/</a>). </p><p class="Standard">Il
245 s'agit de la description d'un tableau de contingence qui croise formes
246 et groupes d'UCI. Les groupes d'UCI sont sélectionnées en fonction de
247 variables illustratives. L'objectif est de comparer ces groupes d'UCI.</p><p class="Standard"> </p><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId695160" /><a id="a__2_5_1__Options"><span style="margin-right: 0.381cm;"> 2.5.1 </span></a>Options</h3><p class="Standard">L'effectif minimum d'une forme sélectionnée peut être paramétré. Par défaut, cette valeur est à 10.</p><p class="Standard"> </p><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId763197" /><a id="a__2_5_2__Résultats"><span style="margin-right: 0.381cm;"> 2.5.2 </span></a>Résultats</h3><p class="Text_20_body">Les mêmes résultats sont produits sur les formes et sur les types.</p><ul><li><p class="P13" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Onglet Spécificités :<span class="odfLiEnd"> </span></p><p class="P13" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Présente
248 l'exposant du seuil de significativité du chi2 qui mesure la force du
249 lien entre la forme et la variable. Par exemple, si une forme est liée
250 à une variable avec un chi2 dont le seuil de significativité est 0,001,
251 la valeur 3 sera notée car 0,001 = 10<span class="T1">-3</span><span class="T2">.</span><span class="odfLiEnd"> </span></p></li><li><p class="P14" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Onglet Effectifs :<span class="odfLiEnd"> </span></p><p class="P14" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Les effectifs<span class="odfLiEnd"> </span></p></li><li><p class="P14" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Onglet Effectifs relatifs :<span class="odfLiEnd"> </span></p><p class="P14" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Les effectifs relatifs en 1000ème<span class="odfLiEnd"> </span></p></li></ul><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId723694" /><a id="a__2_6__AFC_sur_UCI"><span style="margin-right: 0.381cm;"> 2.6 </span></a>AFC sur UCI</h2><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId115880" /><a id="a__2_6_1__Description"><span style="margin-right: 0.381cm;"> 2.6.1 </span></a>Description</h3><p class="Text_20_body">Analyse de texte → AFC sur UCI</p><p class="Text_20_body">Produit une analyse factorielle des correspondances sur un tableau de contingence qui croise formes actives et UCI.</p><p class="Text_20_body">Cette analyse est immature. Il est préférable d'utiliser l'analyse « Comme lexico » sur les UCI.</p><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId425577" /><a id="a__2_6_2__Options"><span style="margin-right: 0.381cm;"> 2.6.2 </span></a>Options</h3><p class="Text_20_body">Pas d'options pour l'instant</p><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId16996" /><a id="a__2_6_3__Résultats"><span style="margin-right: 0.381cm;"> 2.6.3 </span></a>Résultats</h3><p class="Text_20_body">Trois graphiques d'AFC sont proposés : formes actives, formes supplémentaire et variables étoilées.</p><p class="Text_20_body"> </p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId285818" /><a id="a__2_7__Classification"><span style="margin-right: 0.381cm;"> 2.7 </span></a>Classification</h2><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId447946" /><a id="a__2_7_1__Méthode_ALCESTE"><span style="margin-right: 0.381cm;"> 2.7.1 </span></a>Méthode ALCESTE</h3><h4 class="Heading_20_4"><a class="mozTocH4" name="mozTocId767561" /><a id="a__2_7_1_1__Description"><span style="margin-right: 0.381cm;"> 2.7.1.1 </span></a>Description</h4><p class="Standard">Analyse de texte → Classification → méthode ALCESTE</p><p class="Standard">Cette
252 analyse propose une classification hiérarchique descendante selon la
253 méthode ALCESTE (Reinert, 1983, 1986, 1991). La classification peut
254 être menée sur les UCI (classification simple sur UCI) ou sur des
255 segments de textes (Unité de Contexte Élémentaire : UCE). Les
256 classifications sur les UCE peuvent être conduites directement sur
257 celles-ci (classification simple sur UCE) ou sur deux tableaux
258 proposant des regroupements de segments de texte (Unité de
259 Contexte : UC) qui différent par le nombre de variables actives
260 (et donc d'UCE) regroupées par ligne (classification double sur UC).</p><p class="Standard">Voir le détail de la classification ALCESTE en annexe.</p><h4 class="P20"><a class="mozTocH4" name="mozTocId668017" /><a id="a__2_7_1_2__Options"><span style="margin-right: 0.381cm;"> 2.7.1.2 </span></a>Options</h4><!--Next 'div' was a 'text:p'.--><div class="Text_20_body"> <!--Next 'div' is emulating the top hight of a draw:frame.--><div style="height: 0.199cm;"> </div><!--Next 'div' is a draw:frame.--><div style="padding: 0pt; height: 11.359cm; width: 8.269cm; float: left; position: relative; left: 4.256cm;" class="fr2" id="images4"><img style="height: 11.359cm; width: 8.269cm;" alt="" 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" /></div></div><div style="margin: 0pt; padding: 0pt; clear: both; line-height: 0pt; width: 0pt; height: 0pt;"> </div><ul><li><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Utiliser le dictionnaire des expressions :<span class="odfLiEnd"> </span></p><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>voir dictionnaire des expressions<span class="odfLiEnd"> </span></p><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span><span class="odfLiEnd"> </span></p></li><li><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Lemmatisation :<span class="odfLiEnd"> </span></p><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>voir lemmatisation<span class="odfLiEnd"> </span></p><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span><span class="odfLiEnd"> </span></p></li><li><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Classification :<span class="odfLiEnd"> </span></p><ul><li><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">◦.</span>double
261 sur UC : la classification est menée sur deux tableaux qui
262 regroupent sur chaque ligne un certain nombre d'UCE en fonction du
263 nombre de formes actives par ligne des paramètres « taille uc
264 1 » et « taille uc2 »<span class="odfLiEnd"> </span></p></li><li><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">◦.</span>simple sur UCE : la classification est menée sur les UCE<span class="odfLiEnd"> </span></p></li><li><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">◦.</span>simple sur UCI : la classification est menée sur les UCI<span class="odfLiEnd"> </span></p></li></ul></li></ul><p class="Standard"> </p><ul><li><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Nombre de classes terminales de la phase 1 :<span class="odfLiEnd"> </span></p><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Détermine le nombre de classes de la première partie de la classification.<span class="odfLiEnd"> </span></p><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span><span class="odfLiEnd"> </span></p></li><li><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Nombre d'occurrences par UCE :<span class="odfLiEnd"> </span></p><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Permet
265 de choisir la taille des UCE en fonction du nombre d'occurrences
266 qu'elles regroupent. Par défaut, ce calcul est automatique et la taille
267 des UCE est fonction de la taille du corpus. Plus le corpus est
268 important, plus les UCE seront longues. Dans tous les cas, la
269 ponctuation est prise en compte dans le découpage ; la valeur du
270 nombre d'occurrences est donc « un objectif à atteindre » et
271 pas une valeur stricte.<span class="odfLiEnd"> </span></p></li></ul><p class="Standard"> </p><p class="Standard"> </p><ul><li><!--Next 'div' was a 'text:p'.--><div class="P19"> <!--Next 'div' is a draw:frame.--><div style="padding: 0pt; height: 0.534cm; width: 0.496cm; float: left; position: relative; left: -0.499cm;" class="fr1" id="images5"><img style="height: 0.534cm; width: 0.496cm;" alt="" src="data:image/*;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAABGdBTUEAAK/INwWK6QAAABl0RVh0U29mdHdhcmUAQWRvYmUgSW1hZ2VSZWFkeXHJZTwAAAYuSURBVHjaYvz//z/DQAKAACIbnfZhUDzmyaAEZDIBMSO55gAEEBM5mi74MjSyMzHc42FluAt0RBtQiINcswACiJkcn7MyMawT1GVg4BRnYPj3hsGGl5Vh69FXDG+A0n9JNQ8ggEh2NRsTw3xmoH8lXSAYxA6QZZgElOIlx0MAAcREYtAHAiPbHmYxzCHAqDCZZ80QAlTCSWp6AAggkhzAyMjQzwEMdkFLPQYGs9NgDGKDxHQFGWqASviAmJUUMwECiImUhAf0mryUK1CLQi0wyYlBMJANEgMmSsktzgxVQKVcpJgLEEDMxCY8FmDcCwE9K+IdzsAgHg8U/Q/BbFIMbFzPGH4/u8rA+o5Bg4uFYdfx1wyviU2QAAFElAOy1RkmsHIymMuG8TIwa09kOH7qOcOWLacZzpy5w/Dnzz8GWTUrBg7WtQwfL/xil2JjEF50l2EHUNtPqCvxAoAAIuiA874MjszAuBezY2Lgcy5lePfHlGHv3gvA9MDIwMTExPD06RsGAWEZBjE5QYb/748w/H/CoKElwHBp+1OGO0DtfwiZDxBABOOKCWg5myADg5iXCgODUCDDjx9fwOKgIvzfv39g9ocPH8ByYl7KDCC1ZiIMtdAEyULIfIAAYiKQ8BKBCU9f0gdISuQA8x0bAxvbf7DlMAeAsLAwG1iOQTKDAaSWk5lBfZkdQwI0W+K1AyCAcEpe8mMQAPq+j1sZ6BVLO2BmNwaK/mAQEWFlYGb+D4z7P3AsIwNK+N/AavjMTRlAerT4GSr9ZBlkCWVLgADC6YB//xkagJSATDTQcNE4YCHwH+wAEJaX52X49esXGAsIsDKws/8Gin8HqgEmfLEYBpkoNlDU8WZpMFQAJbjxpTWAAGLCle2ABuSL2ANzmYI7kAB55CvEEiCWkmIFpoUfDD9//gSzIXIg/JmBgV2SgU3elgGkV4qTISZXg0EHKMGOq4QECCAmnOU9MPbE/IQZGPjdkCwA4S8MsrJMDN+/f2f49u0bg4oKG8Rihk9QDGQL2jOI+fIwgMyIVGToBQry4AoFgABiwlnehwJ1SDgD0zEvwmAw/sIgKPgTiP8zfP36lUFZ+T+SPIh+D9TDCdRrzgAyg1A9ARBALFjLe1mQJySAsWcCFHmHxa3/gAnvL8Pfv6Bs+AFa6IEwKC38gmR/bi0GQbvTDG/2f2LQ/QeuJzZCC6dfyPYBBBAzenkPjHt/uQxgNCh6AH0iBI33H1D8HZLagWxFRUYGbW0WBm7uH/CogdDfIGxGoF3MTAwcwo8ZPh9j4PWWYeBYdp/hMNQB8BISIICYkbMdMIBWCNmAyns5YJViADXsJzzxQfjfwRaxsX3HYjkyGxglLOwMbAJvGH6//sHA+pxB4/E3hjW3PzF8RC4hAQKICSnbTWDhYuAXCwZGE5cqNOjfQjEy+y3Dtm0PGdzdLzJYWJxjmD37Hg61HyCYS4JBLBhYGHAz8BZpMTSjZ0uAAGJGKe/9QIWOJFC1MFrQf0PCXxmSkl4wvH37A1wOHDv2kSEg4B8DHx9CHhFaQMz0D5gbfjH8B6r9f5tBQ0eA4eK2pwx3YaEAEEAs0PK+nk2UgUHYB5in2UA55g3Ubdhy6X9g9vsCzAH/gInwL7go/v//LzRa/4ITKAQjsdmBVaQPE8P7Q/8YDH4zFAEF90HTwm+AAGIGlfegQkcmF5hPlICWMzMi+fwHUkgg0oCg4C+GzZt/gYvh4ODfDOHhP+GJExMD5Rh/MTCx/mdgE/vH8P04g5S9OMPbNQ8ZLoAcABBAjBf9GB7w6DDIK7WAfMcPVMwCza7I+D+U/gcPBQT+h8ZmgPr+Pyr+DwmRe8AM+e4Sw3PTLQxmQIFXAAHEAmpmMQOTxderQCuYYMHGCC4QoCUDSvBD62I4+z88yJHF0S1HqAfZBWq+QVvRHwACiOXHX4ZpDCcZsj6dBFrFDAwyJma45YzIDkHqwv1HdggMg9QhWcQArbJRGkVQ5v0vDKuhCYwJIIBApnOU6TAYirAzaP76x8ACzI5kd7OIAVc/MDxb+YDhIaTMZngNEECMUJewQ8tqVkr6eUSC/9AyG5SqfwIEEHJEM5LbvyMDwFMuQAAxDnT3HCDAAN6sBt/v91SLAAAAAElFTkSuQmCC" /></div><!--Next 'div' added for floating.--><div style="position: relative; left: -0.499cm;">Vérifiez la taille des UCE dans vos analyses, elle peut rapidement devenir trop importante.</div></div><div style="margin: 0pt; padding: 0pt; clear: both; line-height: 0pt; width: 0pt; height: 0pt;"> </div><p class="P15"> </p></li><li><p class="P15" style="margin-left: 0.499cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Nombre minimum d'UCE par classe :<span class="odfLiEnd"> </span></p><p class="P15" style="margin-left: 0.499cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Permet
272 de choisir le nombre minimum d'UCE par classe. Par défaut, seules les
273 classes regroupant 1/(le nombre de classes terminales de la phase 1)
274 des UCE pour une classification simple, et 1/(2*le nombre de classes
275 terminales de la phase 1) des UCE pour une classification double,
276 seront retenues.<span class="odfLiEnd"> </span></p></li></ul><p class="Standard"> </p><ul><li><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Nombre maximum de formes analysées :<span class="odfLiEnd"> </span></p><p class="P15" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Par
277 défaut, les 1500 formes actives les plus fréquentes et les 1500 formes
278 supplémentaires les plus fréquentes seront retenues. Une forme doit
279 avoir au minimum une fréquence de 4 pour être retenue. Si le corpus à
280 moins de 1500 formes, toutes les formes avec une fréquence strictement
281 supérieure à 3 seront retenues.<span class="odfLiEnd"> </span></p></li></ul><p class="Standard"> </p><p class="Standard"> </p><ul><li><!--Next 'div' was a 'text:p'.--><div class="P19"> <!--Next 'div' is a draw:frame.--><div style="padding: 0pt; height: 0.534cm; width: 0.496cm; float: left; position: relative; left: -0.499cm;" class="fr1" id="images6"><img style="height: 0.534cm; width: 0.496cm;" alt="" src="data:image/*;base64,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" /></div><!--Next 'div' added for floating.--><div style="position: relative; left: -0.499cm;">Ce
282 paramètre a une forte incidence sur la taille des tableaux analysés et
283 donc sur la quantité de mémoire de l'ordinateur mobilisée. Si votre
284 ordinateur n'a pas assez de mémoire pour analyser un corpus, essayez de
285 baisser ce paramètre. Si votre ordinateur possède
286 « suffisamment » de mémoire pour le corpus et que le corpus
287 possède plus de 1500 formes de fréquence > 3, n'hésitez pas à
288 l'augmenter.</div></div><div style="margin: 0pt; padding: 0pt; clear: both; line-height: 0pt; width: 0pt; height: 0pt;"> </div></li><li><p class="P15" style="margin-left: 0.499cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Configuration des clés d'analyse :<span class="odfLiEnd"> </span></p><p class="P15" style="margin-left: 0.499cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Voir clés d'analyse<span class="odfLiEnd"> </span></p></li></ul><h4 class="Heading_20_4"><a class="mozTocH4" name="mozTocId418081" /><a id="a__2_7_1_3__Résultats"><span style="margin-right: 0.381cm;"> 2.7.1.3 </span></a>Résultats</h4><p class="Text_20_body">Les
289 résultats directement disponibles présentent un résumé de la
290 classification (onglet CHD) les profils des classes (onglet Profils),
291 les antiprofils des classes (onglet Antiprofils) et une analyse
292 factorielle des correspondances menées sur le tableau de contingence
293 croisant formes et classes (onglet AFC).</p><!--Next 'div' was a 'text:p'.--><div class="Text_20_body"> <!--Next 'div' is emulating the top hight of a draw:frame.--><div style="height: 0.302cm;"> </div><!--Next 'div' is a draw:frame.--><div style="padding: 0pt; height: 2.88cm; width: 12.088cm; float: left; position: relative; left: 2.187cm;" class="fr2" id="images7"><img style="height: 2.88cm; width: 12.088cm;" alt="" 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" /></div></div><div style="margin: 0pt; padding: 0pt; clear: both; line-height: 0pt; width: 0pt; height: 0pt;"> </div><h5 class="Heading_20_5"><a class="mozTocH5" name="mozTocId373356" /><a id="a__2_7_1_3_1__Options_des_profils"><span style="margin-right: 0.381cm;"> 2.7.1.3.1 </span></a>Options des profils</h5><p class="Text_20_body">A partir d'un clique droit sur une ligne du profil, plusieurs outils complémentaires sont proposés :</p><!--Next 'div' was a 'text:p'.--><div class="Text_20_body"> <!--Next 'div' is emulating the top hight of a draw:frame.--><div style="height: 0.205cm;"> </div><!--Next 'div' is a draw:frame.--><div style="padding: 0pt; height: 4.279cm; width: 4.685cm; float: left; position: relative; left: 0.706cm;" class="fr2" id="images8"><img style="height: 4.279cm; width: 4.685cm;" alt="" 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" /></div></div><div style="margin: 0pt; padding: 0pt; clear: both; line-height: 0pt; width: 0pt; height: 0pt;"> </div><ul><li><p class="P17" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Formes associées : renvoie les mots associées à la forme sélectionnée et leurs effectifs.<span class="odfLiEnd"> </span></p></li><li><p class="P17" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Chi2
294 par classe : crée un graphique qui présente le chi2 d'association
295 de la forme à chacune des classes. Plusieurs formes peuvent être
296 sélectionnées en même temps.<span class="odfLiEnd"> </span></p></li><li><p class="P17" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Chié
297 modalités de la variable : crée un graphique qui représente le
298 chi2 d'association des modalités de la variable sélectionnée à chacune
299 des classes. Nécessite un formatage du type variable_modalité.<span class="odfLiEnd"> </span></p></li><li><p class="P17" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Graph
300 du mot : crée un graph de similitude représentant les
301 cooccurrences dans la classe du mot sélectionné. Voir « analyse de
302 similitude » pour plus de détails.<span class="odfLiEnd"> </span></p></li><li><p class="P17" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Concordancier :
303 propose le concordancier de la (ou des) forme(s) sélectionnée(s). Ce
304 concordancier est disponible pour les UCE de la classe, les UCE
305 classées ou toutes les UCE du corpus.<span class="odfLiEnd"> </span></p></li><li><p class="P17" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Outils du CNRTL : interroge la base de données du Centre Nationale de Ressources Textuelles et Lexicales (<a href="http://www.cnrtl.fr/">http://www.cnrtl.fr/</a>)
306 à partir de la forme sélectionnée (nécessite d'être connecté à
307 Internet). Permet d'obtenir une définition (Lexicographie), les
308 synonymes (Synonymie), les Antonymes (Antonymie), l'étymologie
309 (Etymologie) et la morphologie (Morphologie) de la forme. Les résultats
310 s'affichent dans le navigateur internet par défaut du système.<span class="odfLiEnd"> </span></p></li><li><p class="P17" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Graph
311 de classe : indépendant de la ligne sélectionnée. Il s'agit d'une
312 analyse de similitude menée sur un tableau absence/présence (0/1) qui
313 croise les unités choisies en ligne (UCI ou UCE) et les formes actives
314 de la classe en colonne. La matrice de similitude est construite sur
315 les colonnes (les formes actives de la classe). Par défaut, l'indice de
316 similitude utilisé est la cooccurrence. Les résultats se présentent
317 sous la forme d'un graphe de similitude réduit à un arbre maximum. <span class="odfLiEnd"> </span></p><p class="P17" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Voir « analyse de similitude » pour plus de détails.<span class="odfLiEnd"> </span></p></li><li><p class="P17" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Segments
318 répétés : indépendant de la ligne sélectionnée. Effectifs et
319 tailles des segments répétés de la classe. Préférez les profils des
320 segments répétés.<span class="odfLiEnd"> </span></p></li><li><p class="P17" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>UCE
321 caractéristiques : indépendant de la ligne sélectionnée. Liste les
322 UCE caractéristiques de la classe. Deux scores sont proposés :<span class="odfLiEnd"> </span></p><ul><li><p class="P17" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">◦.</span>absolu :
323 les UCE sont classées en fonction de la somme de chi2 de liaison à la
324 classe des formes actives qu'elles contiennent.<span class="odfLiEnd"> </span></p></li><li><p class="P17" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">◦.</span>Relatif :
325 les UCE sont classées en fonction de la moyenne des chi2 de liaison à
326 la classe des formes actives qu'elles contiennent.<span class="odfLiEnd"> </span></p></li></ul></li></ul><p class="note">Dans le cas d'une classification sur UCI, remplacez UCE par UCI dans la description précédente.</p><h4 class="Heading_20_4"><a class="mozTocH4" name="mozTocId886605" /><a id="a__2_7_1_4__Fichiers_en_sortie"><span style="margin-right: 0.381cm;"> 2.7.1.4 </span></a>Fichiers en sortie</h4><table class="Tableau3" border="0" cellpadding="0" cellspacing="0"><colgroup><col width="185" /><col width="557" /></colgroup><tbody><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A1"><p class="Standard">Répertoire de sortie</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_B1"><p class="Standard">NomDuCorpus_alceste_x</p></td></tr><tr><td colspan="2" style="text-align: left; width: 4.242cm;" class="Tableau3_A2"><p class="Table_20_Contents">Fichiers en sortie :</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">TableUc1.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Le tableau UC1/formes ou UCI/formes ou UCE/formes</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">TableUc2.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Le tableau UC2/formes</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">listeUCE1.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Tableau uce;uc pour les UC1</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">listeUCE2.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Tableau uce;uc pour les UC2</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">profiles.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Profils des classes</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">antiprofiles.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Antiprofils des classes</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">info.txt</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Résumé de la classification</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">uce.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Les uce par classe</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">arbre_1.png</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Dendrogramme de la première CHD</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">arbre_2.png</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Dendrogramme de la seconde CHD</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">dendro1.png</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Dendrogramme final sur UC1</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">dendro2.png</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Dendrogramme final sur UC2</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">classe_mod.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Tableau de contingence formes actives/classes</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">RData.RData</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Résultats dans R</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">tablesup.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Tableau de contingence formes supplémentaires/classes</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">tableet.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Tableau de contingence variables illustratives/classes</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">SbyClasseOut.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Les uce par classe</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">chisqtable.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Chi2 d'association de chaque formes aux classes</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">ptable.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Seuil de significativité des chi2 d'associations de chaque forme aux classes.</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">Analyse.ira</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Fichier Analyse : permet de ré-ouvrir une analyse.</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">AFC2DL.png</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Graph AFC : Variables actives - coordonnées - facteurs 1 / 2</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">AFC2DSL.png</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Graph AFC : variables supplémentaires - coordonnées - facteurs 1 / 2</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">AFC2DEL.png</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Graph AFC : Variables illustratives - Coordonnées - facteur 1 / 2</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">AFC2DCL.png</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Graph AFC : Classes - Coordonnées - facteur 1 / 2</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">AFC2DCoul.png</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Graph AFC : Variables actives - Corrélation - facteur 1 / 2</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">AFC2DCoulSup.png</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Graph AFC : Variables supplémentaires - Corrélation - facteur 1 / 2</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">AFC2DCoulEt.png</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Graph AFC : Variables illustratives - Corrélations - facteur 1 / 2</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">AFC2DCoulCl.png</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Graph AFC : Classes - Corrélations - facteurs 1 / 2</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">liste_graph_afc.txt</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Liste de s graphiques de l'onglet AFC</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">liste_graph_chd.txt</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Liste de graphiques de l'onglet CHD</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">afc_row.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Résultats
327 de l'AFC ; Coordonnées, corrélation, MASS, contribution des formes
328 : voir le manuel de la librairie ca de R pour plus de détail.</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">afc_col.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Résultats
329 de l'AFC ; Coordonnées, corrélation, MASS, contribution des
330 classes : voir le manuel de la librairie ca de R pour plus de détail.</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">afc_facteur.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Résultats de l'AFC ; Valeurs propres, Pourcentage d'inertie extraite et Pourcentage cumulé des facteurs.</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">segments_classes.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Tableau de contingence segments répétés/classes</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">prof_segments.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Profils des segments répétés</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">antiprof_segments.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Antiprofils des segments répétés</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">profil_type.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Profils des types</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">antiprof_type.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Antiprofils des types</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">type_cl.csv</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Tableau de contingence types/classes</p></td></tr><tr><td style="text-align: left; width: 4.242cm;" class="Tableau3_A3"><p class="P4">analyse.db</p></td><td style="text-align: left; width: 12.756cm;" class="Tableau3_A2"><p class="Table_20_Contents">Base de données contenant les résultats</p></td></tr></tbody></table><p class="Standard"> </p><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId82973" /><a id="a__2_7_2__Par_matrice_des_distances"><span style="margin-right: 0.381cm;"> 2.7.2 </span></a>Par matrice des distances</h3><h4 class="Heading_20_4"><a class="mozTocH4" name="mozTocId911832" /><a id="a__2_7_2_1__Description"><span style="margin-right: 0.381cm;"> 2.7.2.1 </span></a>Description</h4><p class="Standard">Produit
331 une classification à partir d'une matrice de distance construite à
332 partir d'un tableau absence/présence qui croise l'unité choisie (UCI ou
333 UCE) et les formes actives. La matrice de distance est construite à
334 partir des lignes de ce tableau (les unités).</p><p class="Text_20_body"> </p><h4 class="P20"><a class="mozTocH4" name="mozTocId470612" /><a id="a__2_7_2_2__Options"><span style="margin-right: 0.381cm;"> 2.7.2.2 </span></a>Options</h4><!--Next 'div' was a 'text:p'.--><div class="Text_20_body"> <!--Next 'div' is emulating the top hight of a draw:frame.--><!--Next 'div' is a draw:frame.--><div style="padding: 0pt; height: 10.98cm; width: 7.876cm; float: left; position: relative; left: 0cm;" class="fr3" id="images10"><img style="height: 10.98cm; width: 7.876cm;" alt="" 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" /></div></div><div style="margin: 0pt; padding: 0pt; clear: both; line-height: 0pt; width: 0pt; height: 0pt;"> </div><ul><li><p class="P16" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Méthode de construction de la matrice des distances :<span class="odfLiEnd"> </span></p><p class="P16" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Permet
335 de choisir l'indice de distance utilisé dans la matrice des distances.
336 Voir la documentation de la fonction dist (librairie stats) de R pour
337 plus de détails sur ces indices. Le fichier traité étant de type
338 absence/présence, seul l'indice « binary » est pertinent. Il
339 s'agit de la distance de Jaccard.<span class="odfLiEnd"> </span></p></li><li><p class="P16" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Analyse :<span class="odfLiEnd"> </span></p><p class="P16" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Deux
340 algorithmes de classification sont proposés :
341 « k-means » par l'intermédiaire de la fonction
342 « pam » et « fuzzy clustering » par
343 l'intermédiaire de la fonction « fanny ». Ces deux fonctions
344 font parties de la librairie cluster de R. Voir la documentation de la
345 librairie cluster pour plus de détails : <a href="http://cran.r-project.org/web/packages/cluster/cluster.pdf">http://cran.r-project.org/web/packages/cluster/cluster.pdf</a><span class="odfLiEnd"> </span></p></li><li><p class="P16" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Classification :<span class="odfLiEnd"> </span></p><p class="P16" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Permet de choisir les unités en ligne : UCE ou UCI<span class="odfLiEnd"> </span></p></li><li><p class="P16" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Nombre maximum de formes analysées :<span class="odfLiEnd"> </span></p><p class="P16" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Voir Méthode ALCESTE → Options<span class="odfLiEnd"> </span></p></li><li><p class="P16" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;">•.</span>Nombre de classes :<span class="odfLiEnd"> </span></p><p class="P16" style="margin-left: 0cm;"><span class="Bullet_20_Symbols" style="display: block; float: left; min-width: 0cm;"><!-- --></span>Nombre de classes souhaitées. Par défaut, 4 classes seront construites.<span class="odfLiEnd"> </span></p></li></ul><p class="Standard"> </p><h4 class="Heading_20_4"><a class="mozTocH4" name="mozTocId556046" /><a id="a__2_7_2_3__Résultats"><span style="margin-right: 0.381cm;"> 2.7.2.3 </span></a>Résultats</h4><p class="Text_20_body">Les résultats se présentent comme les résultats de la méthode ALCESTE. Voir méthode ALCESTE → Résultats.</p><h1 class="Heading_20_1"><a class="mozTocH1" name="mozTocId112924" /><a id="a__3__Analyses_de_tableaux_de_données"><span style="margin-right: 0.381cm;"> 3 </span></a>Analyses de tableaux de données</h1><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId830044" /><a id="a__3_1__Format_des_données_en_entrée"><span style="margin-right: 0.381cm;"> 3.1 </span></a>Format des données en entrée</h2><p class="Standard">Les
346 tableaux de données doivent être du type individus/caractères. Les
347 variables doivent être préférentiellement présentées sous la forme
348 variable_modalité. Dans le cadre des classifications ALCESTE, le
349 tableau d'entrée est transformé en tableau absence/présence (0/1). Il n
350 'est donc généralement pas acceptable que deux colonnes distinctes
351 contiennent des modalités formatées de la même façon. Une étoile
352 peut être introduite devant les modalités qui seront utilisées comme
353 variables illustratives dans les classifications ALCESTE. Cette
354 présentation correspond à un corpus « formaté ».</p><p class="Standard"> </p><p class="Standard">exemple :</p><p class="Standard"> </p><table class="Tableau2" border="0" cellpadding="0" cellspacing="0"><colgroup><col width="101" /><col width="122" /><col width="101" /><col width="100" /></colgroup><tbody><tr class="Tableau21"><td style="text-align: left; width: 2.32cm;" class="Tableau2_A1"><p class="P5">id</p></td><td style="text-align: left; width: 2.796cm;" class="Tableau2_A1"><p class="P5">var1</p></td><td style="text-align: left; width: 2.314cm;" class="Tableau2_A1"><p class="P5">var2</p></td><td style="text-align: left; width: 2.288cm;" class="Tableau2_D1"><p class="P4">…</p></td></tr><tr class="Tableau21"><td style="text-align: left; width: 2.32cm;" class="Tableau2_A2"><p class="P5">1</p></td><td style="text-align: left; width: 2.796cm;" class="Tableau2_A2"><p class="P4">*var1_mod1</p></td><td style="text-align: left; width: 2.314cm;" class="Tableau2_A2"><p class="P4">var2_mod2</p></td><td style="text-align: left; width: 2.288cm;" class="Tableau2_D2"><p class="P4">…</p></td></tr><tr class="Tableau21"><td style="text-align: left; width: 2.32cm;" class="Tableau2_A2"><p class="P5">2</p></td><td style="text-align: left; width: 2.796cm;" class="Tableau2_A2"><p class="P4">*var1_mod2</p></td><td style="text-align: left; width: 2.314cm;" class="Tableau2_A2"><p class="P4">var2_mod1</p></td><td style="text-align: left; width: 2.288cm;" class="Tableau2_D2"><p class="P4">…</p></td></tr><tr class="Tableau21"><td style="text-align: left; width: 2.32cm;" class="Tableau2_A2"><p class="P5">3</p></td><td style="text-align: left; width: 2.796cm;" class="Tableau2_A2"><p class="P4">*var1_mod3</p></td><td style="text-align: left; width: 2.314cm;" class="Tableau2_A2"><p class="P4">var2_mod3</p></td><td style="text-align: left; width: 2.288cm;" class="Tableau2_D2"><p class="P4">…</p></td></tr><tr class="Tableau21"><td style="text-align: left; width: 2.32cm;" class="Tableau2_A2"><p class="P5">4</p></td><td style="text-align: left; width: 2.796cm;" class="Tableau2_A2"><p class="P4">*var1_mod2</p></td><td style="text-align: left; width: 2.314cm;" class="Tableau2_A2"><p class="P4">var2_mod4</p></td><td style="text-align: left; width: 2.288cm;" class="Tableau2_D2"><p class="P4">…</p></td></tr><tr class="Tableau21"><td style="text-align: left; width: 2.32cm;" class="Tableau2_A2"><p class="P5">5</p></td><td style="text-align: left; width: 2.796cm;" class="Tableau2_A2"><p class="P4">*var1_mod3</p></td><td style="text-align: left; width: 2.314cm;" class="Tableau2_A2"><p class="P4">var2_mod6</p></td><td style="text-align: left; width: 2.288cm;" class="Tableau2_D2"><p class="P4">…</p></td></tr><tr class="Tableau21"><td style="text-align: left; width: 2.32cm;" class="Tableau2_A2"><p class="P4">…</p></td><td style="text-align: left; width: 2.796cm;" class="Tableau2_A2"><p class="P4">…</p></td><td style="text-align: left; width: 2.314cm;" class="Tableau2_A2"><p class="P4">…</p></td><td style="text-align: left; width: 2.288cm;" class="Tableau2_D2"><p class="P4">…</p></td></tr></tbody></table><p class="Standard"> </p><p class="Standard"> </p><p class="Standard">Les
355 fichiers acceptés en entrée doivent être au format .xls (Microsoft
356 Excel 97/2003), .csv ou .ods (openoffice, libreoffice, etc...).</p><p class="Standard"> </p><p class="Standard"> </p><!--Next 'div' was a 'text:p'.--><div class="note"> <!--Next 'div' is a draw:frame.--><div style="padding: 0pt; height: 0.534cm; width: 0.496cm; float: left; position: relative; left: -0.499cm;" class="fr1" id="images9"><img style="height: 0.534cm; width: 0.496cm;" alt="" src="data:image/*;base64,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" /></div><!--Next 'div' added for floating.--><div style="position: relative; left: -0.499cm;">Tous
357 les fichiers transmis à R sont au format .csv avec le ';' comme
358 séparateur de champs. IL EST DONC INDISPENSABLE QUE LE FICHIER EN
359 ENTREE NE CONTIENNE AUCUN ';'.</div></div><div style="margin: 0pt; padding: 0pt; clear: both; line-height: 0pt; width: 0pt; height: 0pt;"> </div><p class="note_borderEnd">De façon plus générale, il faut éviter les caractères en dehors des lettres (a-z), des chiffres (0-9) et du tiret bas (_).</p><p class="Standard"> </p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId473852" /><a id="a__3_2__Fréquences"><span style="margin-right: 0.381cm;"> 3.2 </span></a>Fréquences</h2><p class="Standard"> </p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId287592" /><a id="a__3_3__Chi_2"><span style="margin-right: 0.381cm;"> 3.3 </span></a>Chi 2</h2><p class="Standard"> </p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId881862" /><a id="a__3_4__Classification"><span style="margin-right: 0.381cm;"> 3.4 </span></a>Classification</h2><p class="Standard"> </p><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId541874" /><a id="a__3_4_1__Méthode_ALCESTE"><span style="margin-right: 0.381cm;"> 3.4.1 </span></a>Méthode ALCESTE</h3><p class="Standard"> </p><h3 class="Heading_20_3"><a class="mozTocH3" name="mozTocId348003" /><a id="a__3_4_2__Par_matrice_des_distances"><span style="margin-right: 0.381cm;"> 3.4.2 </span></a>Par matrice des distances</h3><p class="Standard"> </p><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId44642" /><a id="a__3_5__AFCM"><span style="margin-right: 0.381cm;"> 3.5 </span></a>AFCM</h2><h2 class="Heading_20_2"><a class="mozTocH2" name="mozTocId714196" /><a id="a__3_6__Graphes"><span style="margin-right: 0.381cm;"> 3.6 </span></a>Graphes</h2><h1 class="Heading_20_1"><a class="mozTocH1" name="mozTocId899808" /><a id="a__4__Bibliographie"><span style="margin-right: 0.381cm;"> 4 </span></a>Bibliographie</h1><p class="Standard"> </p><h1 class="Heading_20_1"><a class="mozTocH1" name="mozTocId286341" /><a id="a__5__Annexes"><span style="margin-right: 0.381cm;"> 5 </span></a>Annexes</h1></body></html>
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