Cluster characterization through a representativity measure

Abstract : Clustering is an unsupervised learning task which provides a decomposition of a dataset into subgroups that summarize the initial base and give information about its structure. We propose to enrich this result by a numerical coefficient that describes the cluster representativity and indicates the extent to which they are characteristic of the whole dataset. It is defined for a specific clustering algorithm, called Outlier Preserving Clustering Algorithm, opca, which detects clusters associated with major trends but also with marginal behaviors, in order to offer a complete description of the inital dataset. The proposed representativity measure exploits the iterative process of opca to compute the typicality of each identified cluster.
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Marie-Jeanne Lesot, Bernadette Bouchon-Meunier. Cluster characterization through a representativity measure. FQAS 2004 - 6th International Conference on Flexible Query Answering Systems, Jun 2004, Lyon, France. pp.446-458, ⟨10.1007/978-3-540-25957-2_35⟩. ⟨hal-01520566⟩



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