Subspace clustering et degrés de typicité d'attributs : une étude prospective

Subspace clustering can offer, beside a decomposition of data into homogeneous and distinct clusters, a characterisation of the subspaces in which the clusters live. This paper explores the possibility of capturing the notion of characteristic features in the framework of typicality degrees , as typical features. To that aim, it discusses the notion of typicality degrees for features and proposes an Alternating Cluster Estimation algorithm, named TbSC, to exploit these degrees within subspace clustering. It illustrates their differences experimentally using simple data sets.