Subspace clustering can offer, beside a decomposition of data into homogeneous and distinct clusters, a charac-terisation 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.