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Extended Fuzzy Clustering Algorithms

Author

Listed:
  • Kaymak, U.
  • Setnes, M.

Abstract

Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applied successfully in various fields including finance and marketing. Despite the successful applications, there are a number of issues that must be dealt with in practical applications of fuzzy clustering algorithms. This technical report proposes two extensions to the objective function based fuzzy clustering for dealing with these issues. First, the (point) prototypes are extended to hypervolumes whose size is determined automatically from the data being clustered. These prototypes are shown to be less sensitive to a bias in the distribution of the data. Second, cluster merging by assessing the similarity among the clusters during optimization is introduced. Starting with an over-estimated number of clusters in the data, similar clusters are merged during clustering in order to obtain a suitable partitioning of the data. An adaptive threshold for merging is introduced. The proposed extensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resulting extended algorithms are given. The properties of the new algorithms are illustrated in various examples.

Suggested Citation

  • Kaymak, U. & Setnes, M., 2000. "Extended Fuzzy Clustering Algorithms," ERIM Report Series Research in Management ERS-2000-51-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:57
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    File URL: https://repub.eur.nl/pub/57/erimrs20001123094510.pdf
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    Citations

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    Cited by:

    1. Nesrin Alptekin, 2014. "Comparison of Turkey and European Union Countries’ Health Indicators by Using Fuzzy Clustering Analysis," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(10), pages 68-74, October.
    2. Nesrin Alptekin, 2014. "Comparison of Turkey and European Union Countries’ Health Indicators by Using Fuzzy Clustering Analysis," International Journal of Business and Social Research, LAR Center Press, vol. 4(10), pages 68-74, October.

    More about this item

    Keywords

    cluster merging; fuzzy clustering; similarity; volume prototypes;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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