Semi-supervised clustering via information-theoretic markov chain aggregation

Sophie Steger, Bernhard Geiger, Marek Śmieja

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


We connect the problem of semi-supervised clustering to constrained Markov aggregation, ie, the task of partitioning the state space of a Markov chain. We achieve this connection by considering every data point in the dataset as an element of the Markov chain's state space, by defining the transition probabilities between states via similarities between corresponding data points, and by incorporating semi-supervision information as hard constraints in a Hartigan-style algorithm. The introduced Constrained Markov Clustering (CoMaC) is an extension of a recent information-theoretic framework for (unsupervised) Markov aggregation to the semi-supervised case. Instantiating CoMaC for certain parameter settings further generalizes two previous information-theoretic objectives for unsupervised clustering. Our results indicate that CoMaC is competitive with the state-of-the-art.
Original languageEnglish
Title of host publicationProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022
Number of pages4
ISBN (Electronic)9781450387132
Publication statusPublished - 25 Apr 2022
Event37th ACM/SIGAPP Symposium On Applied Computing: SAC 2022 - Virtuell, United States
Duration: 25 Apr 202229 Apr 2022


Conference37th ACM/SIGAPP Symposium On Applied Computing
Abbreviated titleSAC 2022
Country/TerritoryUnited States


  • markov aggregation
  • semi-supervised clustering

ASJC Scopus subject areas

  • Software


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