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Evolutionary Rough K-Means Clustering

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Rough Sets and Knowledge Technology (RSKT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5589))

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Abstract

Rough K-means algorithm and its extensions have been useful in situations where clusters do not necessarily have crisp boundaries. Experimentation with the rough K-means algorithm has shown that it provides a reasonable set of lower and upper bounds for a given dataset. Evaluation of clustering obtained from rough K-means using various cluster validity measures has also been promising. However, rough K-means algorithm has not been explicitly shown to provide optimal rough clustering. This paper proposes an evolutionary rough K-means algorithm that minimizes a rough within-group-error. The proposal is different from previous Genetic Algorithms (GAs) based rough clustering, as it combines the efficiency of rough K-means algorithm with the optimization ability of GAs. The evolutionary rough K-means algorithm provides flexibility in terms of the optimization criterion. It can be used for optimizing rough clusters based on different criteria.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computing Science, Saint Mary’s University, Halifax, Nova Scotia, B3H 3C3, Canada

    Pawan Lingras

Authors
  1. Pawan Lingras
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Editor information

Editors and Affiliations

  1. Faculty of Engineering and Surveying and Engineering, University of Southern Queensland, QLD 4350, Australia

    Peng Wen

  2. School of Information Technology, Queensland University of Technology, QLD 4001, Brisbane, Australia

    Yuefeng Li

  3. Institute of Mathematics, Warsaw University of Technology, Koszykowa 86, 02008, Warsaw, Poland

    Lech Polkowski

  4. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Yiyu Yao

  5. Faculty of Medicine, Department of Medical Informatics, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shusaku Tsumoto

  6. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

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© 2009 Springer-Verlag Berlin Heidelberg

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Lingras, P. (2009). Evolutionary Rough K-Means Clustering. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_9

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  • DOI: https://doi.org/10.1007/978-3-642-02962-2_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02961-5

  • Online ISBN: 978-3-642-02962-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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