Abstract
DIVCLUS-T is a descendant hierarchical clustering method based on the same monothetic approach than classification and regression trees but from an unsupervised point of view. The aim is not to predict a continuous variable (regression) or a categorical variable (classification) but to construct a hierarchy. The dendrogram of the hierarchy is easy to interpret and can be read as decision tree. An example of this new type of dendrogram is given on a small categorical dataset. DIVCLUS-T is then compared empirically with two polythetic clustering methods: the Ward ascendant hierarchical clustering method and the k-means partitional method. The three algorithms are applied and compared on six databases of the UCI Machine Learning repository.
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References
BREIMAN, L., FRIEDMAN, J.H., OLSHEN, R.A. and STONE, C.J. (1984): Classification and regression Trees, C.A: Wadsworth.
CHAVENT, M. (1998): A monothetic clustering method. Pattern Recognition Letters, 19, 989–996.
CHAVENT, M., GUINOT, C., LECHEVALLIER Y. and TENENHAUS, M. (1999): Méthodes divisives de classification et segmentation non supervisée: recherche d’une typologie de la peau humaine saine. Revue Statistique Appliquée, XLVII(4), 87–99.
CHAVENT, M., DING, Y., FU, L., STOLOWY H., and WANG, H. (2005): Disclosure and Determinants Studies: An extension Using the Divisive Clustering Method (DIV). European Accounting Review, to publish.
CHAVENT, M., BRIANT, O. and LECHEVALLIER, Y. (2006): DIVCLUS-T: a new descendant hierarchical clustering method. Internal report U-05-15, Laboratoire de Mathématiques Appliquées de Bordeaux.
HETTICH, S., BLAKE, C.L. and MERZ, C.J. (1998): UCI Repository of machine learning databases, http://www.ics.uci.edu/mlearn/MLRepository.html. Irvine, CA: University of California, Department of Information and Computer Science.
MORGAN, J.N. and SONQUIST, J.A. (1963): Problems in the analysis of survey data, and proposal. J. Aler. Statist. Assoc., 58, 415–434.
PIRCON, J.-Y. (2004): La classification et les processus de Poisson pour de nouvelles méthodes de partitionnement. Phd Thesis, Facultés Universitaires Notre-Dame de la Paix, Belgium.
SAPORTA, G. (1990): Probabilités Analyse des données et Statistique, Editions TECHNIP.
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Chavent, M., Lechevallier, Y. (2006). Empirical Comparison of a Monothetic Divisive Clustering Method with the Ward and the k-means Clustering Methods. In: Batagelj, V., Bock, HH., Ferligoj, A., Žiberna, A. (eds) Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-34416-0_10
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DOI: https://doi.org/10.1007/3-540-34416-0_10
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