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JACIII Vol.19 No.6 pp. 900-906
doi: 10.20965/jaciii.2015.p0900
(2015)

Paper:

On Hierarchical Linguistic-Based Clustering

Naohiko Kinoshita*, Yasunori Endo**, and Akira Sugawara***

*Graduate School of Systems and Information Engineering, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

**Faculty of Engineering, Information and Systems, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

***Canon Inc.
3-30-2 Shimomaruko, Ota, Tokyo 146-8501, Japan

Received:
May 1, 2015
Accepted:
October 12, 2015
Published:
November 20, 2015
Keywords:
clustering, fuzzy reasoning, soft computing, linguistic-based clustering
Abstract

Clustering is representative unsupervised classification. Many researchers have proposed clustering algorithms based on mathematical models – methods we call model-based clustering. Clustering techniques are very useful for determining data structures, but model-based clustering is difficult to use for analyzing data correctly because we cannot select a suitable method unless we know the data structure at least partially. The new clustering algorithm we propose introduces soft computing techniques such as fuzzy reasoning in what we call linguistic-based clustering, whose features are not incident to the data structure. We verify the method’s effectiveness through numerical examples.

References
  1. [1] S. Miyamoto, “Introduction to Cluster analysis,” Morikita-Shuppan, Tokyo, 1999 (in Japanese).
  2. [2] C. P. Pappis and C. I. Siettos, “Fuzzy Reasoning,” Springer, Search Methodologies chapter 15, 2005.
  3. [3] W. V. Leekwijck and E. E. Kerre, “Defuzzification: criteria and classification,” Fuzzy Sets and Systems, Vol.108, pp. 159-178, 1999.
  4. [4] N. Honda and A. Osato, “Introduction to Fuzzy Engineering,” Kaibundo, Tokyo, 1995.
  5. [5] E. H. Mamdani, “Application of fuzzy algorithms for control of simple dynamic plant,” Proc. of Institution of Electrical Engineers, Vol.121, No.12, pp. 1585-1588, 1974.
  6. [6] M. Mizumoto, “Fuzzy controls by product-sum gravity method,” Liu and Mizumoto (Eds.), Advancement of Fuzzy Theory and Systems in China and Japan, pp. 1-4, 1990.
  7. [7] L. Hubertm and P. Arabie, “Comparing Partitions,” J. of Classification, Vol.2, pp. 193-218, 1985.

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Last updated on Nov. 20, 2017