An Ensemble and Multi-View Clustering Method Based on Kolmogorov Complexity

Juan Zamora, Jérémie Sublime

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The ability to build more robust clustering from many clustering models with different solutions is relevant in scenarios with privacy-preserving constraints, where data features have a different nature or where these features are not available in a single computation unit. Additionally, with the booming number of multi-view data, but also of clustering algorithms capable of producing a wide variety of representations for the same objects, merging clustering partitions to achieve a single clustering result has become a complex problem with numerous applications. To tackle this problem, we propose a clustering fusion algorithm that takes existing clustering partitions acquired from multiple vector space models, sources, or views, and merges them into a single partition. Our merging method relies on an information theory model based on Kolmogorov complexity that was originally proposed for unsupervised multi-view learning. Our proposed algorithm features a stable merging process and shows competitive results over several real and artificial datasets in comparison with other state-of-the-art methods that have similar goals.

Original languageEnglish
Article number371
Issue number2
StatePublished - Feb 2023


  • Kolmogorov complexity
  • clustering
  • information theory
  • multi-view learning


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