Ensemble Based Gustafson Kessel Fuzzy Clustering

  • Achmad Fauzi Bagus Firmansyah Politeknik Statistika STIS
  • Setia Pramana Politeknik Statistika STIS
DOI: https://doi.org/10.21108/jdsa.2018.1.6

Abstract

Fuzzy clustering is a clustering method whcih allows an object to belong to two or more cluster by combining hard-clustering and fuzzy membership matrix. Two popular algorithms used in fuzzy clustering are Fuzzy C-Means (FCM) and Gustafson Kessel (GK). The FCM use Euclideans distance for determining cluster membership, while GK use Fuzzy Covariance Matrix that considering covariance between variables. Although GK perform better, it has some drawbacks on handling linearly correlated data, and as FCM the algorithm produce unstable result due to random initialization. These drawbacks can be overcame by using improved covariance estimation and cluster ensemble, respectively. This research presents the implementation of improved covariance estimation and cluster ensemble on GK method and compare it with FCM-Ensemble.

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Published
2018-07-30
How to Cite
Firmansyah, A., & Pramana, S. (2018, July 30). Ensemble Based Gustafson Kessel Fuzzy Clustering. Journal of Data Science and Its Applications, 1(1), 1-9. https://doi.org/https://doi.org/10.21108/jdsa.2018.1.6
Issue
Vol 1 No 1 (2018): Journal of Data Science and Its Applications
Section
Articles
Copyright (c) 2018 Journal of Data Science and Its Applications

Copyright (c) 2018 Journal of Data Science and Its Applications (JDSA)

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