Clustering is an unsupervised learning task which provides a decomposition of a dataset into subgroups that summarize the initialbase and give information about its structure. We propose to enrich this result by a numerical coefficient that describes the cluster representativityand indicates the extent to which they are characteristic of the whole dataset. It is defined for a specific clustering algorithm, called OutlierPreserving Clustering Algorithm, opca, which detects clusters associated with major trends but also with marginal behaviors, in order tooffer a complete description of the inital dataset. The proposed representativity measure exploits the iterative process of opca to computethe typicality of each identified cluster.
CITATION STYLE
Lesot, M. J., & Bouchon-Meunier, B. (2004). Cluster characterization through a representativity measure. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3055, pp. 446–458). Springer Verlag. https://doi.org/10.1007/978-3-540-25957-2_35
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