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JACIII Vol.16 No.7 pp. 831-840
doi: 10.20965/jaciii.2012.p0831
(2012)

Paper:

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Hard c-Means Using Quadratic Penalty-Vector Regularization for Uncertain Data

Yasunori Endo*, Arisa Taniguchi**, and Yukihiro Hamasuna***

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

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

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

Received:
December 28, 2011
Accepted:
September 25, 2012
Published:
November 20, 2012
Creative Commons License
Keywords:
clustering, uncertain data, hard c-means, quadratic penalty-vector regularization, sequential extraction
Abstract
Clustering is an unsupervised classification technique for data analysis. In general, each datum in real space is transformed into a point in a pattern space to apply clustering methods. Data cannot often be represented by a point, however, because of its uncertainty, e.g., measurement error margin and missing values in data. In this paper, we will introduce quadratic penalty-vector regularization to handle such uncertain data using Hard c-Means (HCM), which is one of the most typical clustering algorithms. We first propose a new clustering algorithm called hard c-means using quadratic penalty-vector regularization for uncertain data (HCMP). Second, we propose sequential extraction hard c-means using quadratic penalty-vector regularization (SHCMP) to handle datasets whose cluster number is unknown. Furthermore, we verify the effectiveness of our proposed algorithms through numerical examples.
Cite this article as:
Y. Endo, A. Taniguchi, and Y. Hamasuna, “Hard c-Means Using Quadratic Penalty-Vector Regularization for Uncertain Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.7, pp. 831-840, 2012.
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References
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