Entropy-based fuzzy clustering and fuzzy modeling

J. Yao, M. Dash, S. T. Tan, Huan Liu

Research output: Contribution to journalArticle

134 Citations (Scopus)

Abstract

Fuzzy clustering is capable of finding vague boundaries that crisp clustering fails to obtain. But time complexity of fuzzy clustering is usually high, and the need to specify complicated parameters hinders its use. In this paper, an entropy-based fuzzy clustering method is proposed. It automatically identifies the number and initial locations of cluster centers. It calculates the entropy at each data point and selects the data point with minimum entropy as the first cluster center. Next it removes all data points having similarity larger than a threshold with the chosen cluster center. This process is repeated till all data points are removed. Unlike previous methods of its kind, it does not need to revise entropy value for each data point after a cluster center is determined. This saves a lot of time. Also it requires just two parameters that are easy to specify. It is able to find the natural clusters in the data. The clustering method is also extended to construct a rule-based fuzzy model. A new way of estimating initial membership functions for fuzzy sets is presented. The experimental results show that the fuzzy model is good in predicting output variable values.

Original languageEnglish (US)
Pages (from-to)381-388
Number of pages8
JournalFuzzy Sets and Systems
Volume113
Issue number3
StatePublished - Aug 1 2000
Externally publishedYes

Fingerprint

Fuzzy Modeling
Fuzzy clustering
Fuzzy Clustering
Entropy
Fuzzy Model
Clustering Methods
Membership functions
Fuzzy sets
Membership Function
Time Complexity
Fuzzy Sets
Modeling
Two Parameters
Clustering
Calculate
Output
Experimental Results

Keywords

  • Cluster analysis
  • Entropy
  • Fuzzy sets

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems and Management
  • Statistics, Probability and Uncertainty
  • Electrical and Electronic Engineering
  • Statistics and Probability

Cite this

Yao, J., Dash, M., Tan, S. T., & Liu, H. (2000). Entropy-based fuzzy clustering and fuzzy modeling. Fuzzy Sets and Systems, 113(3), 381-388.

Entropy-based fuzzy clustering and fuzzy modeling. / Yao, J.; Dash, M.; Tan, S. T.; Liu, Huan.

In: Fuzzy Sets and Systems, Vol. 113, No. 3, 01.08.2000, p. 381-388.

Research output: Contribution to journalArticle

Yao, J, Dash, M, Tan, ST & Liu, H 2000, 'Entropy-based fuzzy clustering and fuzzy modeling', Fuzzy Sets and Systems, vol. 113, no. 3, pp. 381-388.
Yao J, Dash M, Tan ST, Liu H. Entropy-based fuzzy clustering and fuzzy modeling. Fuzzy Sets and Systems. 2000 Aug 1;113(3):381-388.
Yao, J. ; Dash, M. ; Tan, S. T. ; Liu, Huan. / Entropy-based fuzzy clustering and fuzzy modeling. In: Fuzzy Sets and Systems. 2000 ; Vol. 113, No. 3. pp. 381-388.
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