Sparse clustering via the Deterministic Information Bottleneck algorithm

• URL: http://arxiv.org/abs/2601.20628v1
• Date: Wed, 28 Jan 2026 14:05:44 GMT
• Title: Sparse clustering via the Deterministic Information Bottleneck algorithm
• Authors: Efthymios Costa, Ioanna Papatsouma, Angelos Markos,
• Abstract summary: When a cluster structure is confined to a subset of the feature space, traditional clustering techniques face unprecedented challenges.<n>We present an information-theoretic framework that overcomes the problems associated with sparse data, allowing for joint feature weighting and clustering.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Our proposal constitutes a competitive alternative to existing clustering algorithms for sparse data, as demonstrated through simulations on synthetic data. The effectiveness of our method is established by an application on a real-world genomics data set.

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