Sparse clustering via the Deterministic Information Bottleneck algorithm

ArXi:2601.20628v3 Announce Type: replace-cross Cluster analysis relates to the task of assigning objects into groups which ideally present some desirable characteristics. When a cluster structure is confined to a subset of the feature space, traditional clustering techniques face unprecedented challenges. We present an information theoretic framework that overcomes the problems associated with sparse data, allowing for joint feature weighting and clustering.