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Contribution à la classification de données binaires et qualitatives

Abstract : aWe propose several clustering methods which are specific of binary and categorical data. Each time, we try to keep to the initial data structure. These methods supply partition optimising criteria defined with absolute value distance or L1 distance. The advantage of this approach is to give results easy to interpret in regard of initial data. Then, we define an inertia on binary space. This binary inertia behaves as an ordinary inertia : a relation of the Huyghens type and a relation of decomposition of the inertia are demonstrated. The clustering method and the crossed clustering method for binary data could be replaced in a more usual context. They respectively optimise an inertia criteria and a measure of information. An agglomerative hierarchical method for binary data is also proposed. Then, we studied a principal components analysis for binary data. This analysis, which is defined with binary factors, can be used to find homogeneous submatrix. Every methods proposed here have been programmed and integrated in SICLA system
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Submitted on : Tuesday, April 24, 2018 - 4:07:51 PM
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  • HAL Id : tel-01776880, version 1



Franck Marchetti. Contribution à la classification de données binaires et qualitatives. Autre [cs.OH]. Université Paul Verlaine - Metz, 1989. Français. ⟨NNT : 1989METZ007S⟩. ⟨tel-01776880⟩



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