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Bipartite Split-Merge Evolutionary Clustering

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Book cover Agents and Artificial Intelligence (ICAART 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11978))

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Abstract

We propose a split-merge framework for evolutionary clustering. The proposed clustering technique, entitled Split-Merge Evolutionary Clustering is supposed to be more robust to concept drift scenarios by providing the flexibility to consider at each step a portion of the data and derive clusters from it to be used subsequently to update the existing clustering solution. The proposed framework is built around the idea to model two clustering solutions as a bipartite graph, which guides the update of the existing clustering solution by merging some clusters with ones from the newly constructed clustering while others are transformed by splitting their elements among several new clusters. We have evaluated and compared the discussed evolutionary clustering technique with two other state of the art algorithms: a bipartite correlation clustering (PivotBiCluster) and an incremental evolving clustering (Dynamic split-and-merge).

This work is part of the research project “Scalable resource efficient systems for big data analytics” funded by the Knowledge Foundation (grant: 20140032) in Sweden.

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Notes

  1. 1.

    Scikit-learn is a Python library for data mining and data analysis.

  2. 2.

    https://gitlab.com/machine_learning_vm/clustering_techniques.

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Author information

Authors and Affiliations

  1. Blekinge Institute of Technology, Karlskrona, Sweden

    Veselka Boeva & Vishnu Manasa Devagiri

  2. Technical University of Sofia, Plovdiv, Bulgaria

    Milena Angelova

  3. EluciDATA Lab, Sirris, Brussels, Belgium

    Elena Tsiporkova

Authors
  1. Veselka Boeva
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  2. Milena Angelova
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  3. Vishnu Manasa Devagiri
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  4. Elena Tsiporkova
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Corresponding author

Correspondence to Milena Angelova .

Editor information

Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  2. LIACC, University of Porto, Porto, Portugal

    Ana Paula Rocha

  3. ICREA, Institute of Evolutionary Biology, Barcelona, Spain

    Luc Steels

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Boeva, V., Angelova, M., Devagiri, V.M., Tsiporkova, E. (2019). Bipartite Split-Merge Evolutionary Clustering. In: van den Herik, J., Rocha, A., Steels, L. (eds) Agents and Artificial Intelligence. ICAART 2019. Lecture Notes in Computer Science(), vol 11978. Springer, Cham. https://doi.org/10.1007/978-3-030-37494-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-37494-5_11

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  • Online ISBN: 978-3-030-37494-5

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