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This paper addresses the problem of cluster characterization by selecting a subset of the most relevant features for each cluster from a categorical dataset in an autonomous way. The proposed autonomous model is based on the Relational Topological Clustering (RTC) associated with a statistical test which allows to detect the most important variables in an automatic way without setting any parameters...
This paper introduces a new topological clustering formalism, dedicated to categorical data arising in the form of a binary matrix or a sum of binary matrices. The proposed approach is based on the principle of the Kohonen's model (conservation of topological order) and uses the Relational Analysis formalism by optimizing a cost function defined as a Condorcet criterion. We propose an hybrid algorithm,...
Several aspects could affect the existing machine learning algorithms. One of these aspects is related to unbalanced classes in which the number of observations belonging to a class, greatly exceeds the observations in other classes. We propose in this paper an under-sampling method which uses self-organizing map to cluster the majority class guided with minority class. The proposed approach has been...
This paper studies the extension of the Modularity measure for categorical data clustering. It first shows the relational data presentation and establishes the relationship between the extended Modularity and the Relational Analysis criterion. Two extensions are presented in this work: the early integration and the intermediate integration approaches. The proposed Modularity measure introduces an...
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