, L'ensemble des travaux décrits dans cet article permettra d'obtenir un algorithme complet de "classification à base de clustering" basé sur un algorithme de k-moyennes qui aura été "supervisé

. Références,

J. P. Pages, F. Cailliez, and Y. Escoufier, Analyse factorielle : un peu d'histoire et de géométrie, Revue de Statistique Appliquée, vol.XXVII, pp.5-28, 1979.

A. K. Jain, M. N. Murty, and P. J. Flynn, Data clustering : A review, pp.264-323, 1999.

A. Cornuéjols and L. Miclet, Apprentissage artificiel : concepts et algorithmes, 2010.

J. Quinlan, C4. 5 : programs for machine learning, vol.1, 1993.

L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and regression trees, 1984.

V. N. Vapnik, The Nature of Statistical Learning Theory, 1995.

S. B. Kotsiantis, Supervised machine learning : A review of classification techniques, Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering, pp.3-24, 2007.

S. Haykin, Neural Networks : A Comprehensive Foundation, 1998.

R. Caruana and A. Niculescu-mizil, An Empirical Comparison of Supervised Learning Algorithms, Proceedings of the 23rd International Conference on Machine Learning, pp.161-168, 2006.

M. Kabir, K. Md-monirul-islam, and . Murase, A new wrapper feature selection approach using neural network, Neurocomputing, vol.73, issue.16, pp.3273-3283, 2010.

G. Fung, S. Sandilya, and R. B. Rao, Rule extraction from linear support vector machines, Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp.32-40, 2005.

R. Vilalta and I. Rish, A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes, Lecture Notes in Computer Science, vol.2837, pp.444-455, 2003.

A. A. Gill, G. D. Smith, and A. J. Bagnall, Improving Decision Tree Performance Through Inductionand Cluster-Based Stratified Sampling. IDEAL, volume 3177 of Lecture Notes in Computer Science, pp.339-344, 2004.

M. Hassan and R. Kotagiri, A new approach to enhance the performance of decision tree for classifying gene expression data, BMC proceedings, vol.3, 2013.

H. Sami, V. Al-harbi, and . Smith, Adapting k-means for supervised clustering, Applied Intelligence, vol.24, issue.3, pp.219-226, 2006.

J. Aguilar, R. Ruiz, C. José, R. Riquelme, and . Giràldez, Snn : A supervised clustering algorithm, Engineering of Intelligent Systems, pp.207-216, 2001.

N. Slonim and N. Tishby, Agglomerative information bottleneck, pp.617-623, 1999.

N. Tishby, C. Fernando, W. Pereira, and . Bialek, The information bottleneck method. arXiv preprint physics/0004057, 1999.

H. Cevikalp, D. Larlus, and F. Jurie, A supervised clustering algorithm for the initialization of rbf neural network classifiers, Signal Processing and Communications Applications, pp.1-4, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00203762

N. Christoph-f-eick, Z. Zeidat, and . Zhao, Supervised clustering algorithms and benefits, Tools with Artificial Intelligence, 2004. ICTAI 2004. 16th IEEE International Conference on, pp.774-776, 2004.

F. Ocegueda, -. Hernandez, and R. Vilalta, An Empirical Study of the Suitability of Class Decomposition for Linear Models : When Does It Work Well ?, SIAM 2013

J. Wu, H. Xiong, and J. Chen, COG : local decomposition for rare class analysis, Data Min Knowl Disc (DMKD), pp.191-220, 2009.

A. K. Jain, Data Clustering : 50 Years Beyond Kmeans, Pattern Recogn. Lett, pp.651-666, 2009.

G. Milligan and M. Cooper, A study of standardization of variables in cluster analysis, Journal of Classi cation, vol.5, issue.2, pp.181-204, 1988.

E. M. Celebi, A. Hassan, P. A. Kingravi, and . Vela, A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm, Journal of Expert Systems with Applications, vol.40, issue.1, pp.200-210, 2013.

A. Ismaili, O. Lemaire, V. Cornuéjols, and A. , Supervised pre-processings are useful for supervised clustering, Springer Series Studies in Classification, Data Analysis, and Knowledge Organization, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01564565

A. Ismaili, O. Lemaire, V. , and C. A. , A supervised methodology to measure the variables contribution to a clustering, International Conference on Neural Information Processing (ICONIP), 2014.
URL : https://hal.archives-ouvertes.fr/hal-01565541

V. Lemaire, A. Oumaima, A. Ismaili, and . Cornuéjols, An Initialization Scheme for Supervized K-means, International Joint Conference on Neural Networks (IJCNN), p.2015
URL : https://hal.archives-ouvertes.fr/hal-01558021