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ClusPath: a temporal-driven clustering to infer typical evolution paths.

, , , and . Data Min. Knowl. Discov., 30 (5): 1324-1349 (2016)

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Mesurer l'intérêt des règles d'association., , , , , and . EGC (Ateliers), volume E-5 of RNTI, page 421-426. Cépaduès-Éditions, (2005)Association Rule Interestingness Measures: Experimental and Theoretical Studies., , , and . Quality Measures in Data Mining, volume 43 of Studies in Computational Intelligence, Springer, (2007)Boosting Neighborhood-Based Classifiers., , and . ICML, page 505-512. Morgan Kaufmann, (2001)Using Local Node Information in Decision Trees: Coupling a Local Labeling Rule with an Off-centered Entropy., , , and . DMIN, page 117-123. CSREA Press, (2008)ClusPath: a temporal-driven clustering to infer typical evolution paths., , , and . Data Min. Knowl. Discov., 30 (5): 1324-1349 (2016)A Clustering of Interestingness Measures., , and . Discovery Science, volume 3245 of Lecture Notes in Computer Science, page 290-297. Springer, (2004)MaxMin Linear Initialization for Fuzzy C-Means., , , and . MLDM (1), volume 10934 of Lecture Notes in Computer Science, page 1-15. Springer, (2018)All-Monotony: A Generalization of the All-Confidence Antimonotony., , , and . ICMLA, page 759-764. IEEE Computer Society, (2009)Une approche ensembliste inspirée du boosting en classification non supervisée., , and . EGC, volume RNTI-E-11 of Revue des Nouvelles Technologies de l'Information, page 361-372. Cépaduès-Éditions, (2008)Ensemble prédicteur fondé sur les cartes auto-organisatrices adapté aux données volumineuses., and . EGC, volume RNTI-E-9 of Revue des Nouvelles Technologies de l'Information, page 473-484. Cépaduès-Éditions, (2007)