preprint · arXiv (Cornell University) · 2026

Sparse clustering via the Deterministic Information Bottleneck algorithm

Efthymios Costa, Ioanna Papatsouma, Angelos Markos

Cited by 0Open access

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.

Cite this paper

APA 7

Costa, E., Papatsouma, I., & Markos, A. (2026). Sparse clustering via the Deterministic Information Bottleneck algorithm. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2601.20628

MLA 9

Costa, Efthymios, et al. “Sparse clustering via the Deterministic Information Bottleneck algorithm.” arXiv (Cornell University), 2026, https://doi.org/10.48550/arxiv.2601.20628.

Chicago

Costa, Efthymios, Ioanna Papatsouma, and Angelos Markos. 2026. “Sparse clustering via the Deterministic Information Bottleneck algorithm.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.2601.20628.

Harvard

Costa, E., Papatsouma, I. and Markos, A. (2026) ‘Sparse clustering via the Deterministic Information Bottleneck algorithm’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.2601.20628.

BibTeX

@misc{costa2026sparse,
  title = {{Sparse clustering via the Deterministic Information Bottleneck algorithm}},
  author = {Costa, Efthymios and Papatsouma, Ioanna and Markos, Angelos},
  journal = {arXiv (Cornell University)},
  year = {2026},
  doi = {10.48550/arxiv.2601.20628}
}

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