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JACIII Vol.19 No.5 pp. 624-631
doi: 10.20965/jaciii.2015.p0624
(2015)

Paper:

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Fuzzy c-Means with Quadratic Penalty-Vector Regularization Using Kullback-Leibler Information for Uncertain Data

Naohiko Kinoshita*, Yasunori Endo**, and Yukihiro Hamasuna***

*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

***Department of Informatics, Kindai University
3-4-1 Kowakae, Higashiosaka, Osaka 577-8502, Japan

Received:
January 31, 2015
Accepted:
June 16, 2015
Published:
September 20, 2015
Creative Commons License
Keywords:
clustering, fuzzy c-means, EM algorithm, uncertainty, K-L information
Abstract
Clustering, a highly useful unsupervised classification, has been applied in many fields. When, for example, we use clustering to classify a set of objects, it generally ignores any uncertainty included in objects. This is because uncertainty is difficult to deal with and model. It is desirable, however, to handle individual objects as is so that we may classify objects more precisely. In this paper, we propose new clustering algorithms that handle objects having uncertainty by introducing penalty vectors. We show the theoretical relationship between our proposal and conventional algorithms verifying the effectiveness of our proposed algorithms through numerical examples.
Cite this article as:
N. Kinoshita, Y. Endo, and Y. Hamasuna, “Fuzzy c-Means with Quadratic Penalty-Vector Regularization Using Kullback-Leibler Information for Uncertain Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.5, pp. 624-631, 2015.
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References
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Last updated on Oct. 01, 2024