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


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

May 1, 2015
October 12, 2015
Online released:
November 20, 2015
November 20, 2015
clustering, fuzzy reasoning, soft computing, linguistic-based clustering

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.

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Last updated on May. 26, 2017