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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Lesot, MJ., Bouchon-Meunier, B. (2004). Cluster Characterization through a Representativity Measure. In: Christiansen, H., Hacid, MS., Andreasen, T., Larsen, H.L. (eds) Flexible Query Answering Systems. FQAS 2004. Lecture Notes in Computer Science(), vol 3055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25957-2_35
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DOI: https://doi.org/10.1007/978-3-540-25957-2_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22160-9
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