Uncertain Fuzzy Clustering: Insights and Recommendations

  • Authors:

  • F. Chung-Hoon Rhee

  • Affiliations:

  • Hanyang Univ.

  • Venue:
  • IEEE Computational Intelligence Magazine
  • Year:
  • 2007

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Abstract

In this article, interval type-2 fuzzy sets were used to model the uncertainty that is associated with the various parameters in objective function-based clustering. The purpose was to represent and manage the uncertainty in the cluster memberships by incorporating interval type-2 fuzzy sets. As a result, interval type-2 clustering methods were obtained by modifying the prototype-updating and hard-partitioning procedures in the type-1 fuzzy objective function-based clustering. As a consequence, the management of uncertainty by an interval type-2 fuzzy approach aids cluster prototypes to converge to a more desirable location than a type-1 fuzzy approach. Several examples illustrated the effectiveness of interval type-2 fuzzy approach methods. Furthermore, the uncertainty associated with the parameters for other existing clustering algorithms can be considered in the development of several other interval type-2 clustering algorithms. They are currently under investigation