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JACIII Vol.15 No.1 pp. 68-75
doi: 10.20965/jaciii.2011.p0068
(2011)

Paper:

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Fuzzy c-Means Clustering for Data with Clusterwise Tolerance Based on L2- and L1-Regularization

Yukihiro Hamasuna*,**, Yasunori Endo**, and Sadaaki Miyamoto**

*Research Fellow of the Japan Society for the Promotion of Science

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

Received:
February 15, 2010
Accepted:
April 22, 2010
Published:
January 20, 2011
Creative Commons License
Keywords:
fuzzy c-means clustering, clusterwise tolerance, L2-regularization, L1-regularization, fuzzy classification function
Abstract
Detecting various kinds of cluster shape is an important problem in the field of clustering. In general, it is difficult to obtain clusters with different sizes or shapes by single-objective function. From that sense, we have proposed the concept of clusterwise tolerance and constructed clustering algorithms based on it. In the field of data mining, regularization techniques are used in order to derive significant classifiers. In this paper, we propose another concept of clusterwise tolerance from the viewpoint of regularization. Moreover, we construct clustering algorithms for data with clusterwise tolerance based on L2- and L1-regularization. After that, we describe fuzzy classification functions of proposed algorithms. Finally, we show the effectiveness of proposed algorithms through numerical examples.
Cite this article as:
Y. Hamasuna, Y. Endo, and S. Miyamoto, “Fuzzy c-Means Clustering for Data with Clusterwise Tolerance Based on L2- and L1-Regularization,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.1, pp. 68-75, 2011.
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