NDLI: Comparison of tolerant fuzzy c-means clustering with L2- and L1-regularization

Content Provider	IEEE Xplore Digital Library
Author	Yukihiro, H. Yasunori, E. Sadaaki, M.
Copyright Year	2009
Description	Author affiliation: Dept. of Risk Engineering, Fac., of Sys. and Info. Eng., University of Tsukuba, 1-1-1 Tennodai, Ibaraki 305-8573, Japan (Yasunori, E.; Sadaaki, M.) \|\| Research Fellow of the Japan Society for the Promotion of Science, JSPS, 6 Ichibancho, Chiyoda-ku, Tokyo 102-8471, Japan (Yukihiro, H.)
Abstract	In this paper, we will propose two types of tolerant fuzzy c-means clustering with regularization terms. One is L2-regularization term and the other is L1-regularization one for tolerance vector. Introducing a concept of clusterwise tolerance, we have proposed tolerant fuzzy c-means clustering from the viewpoint of handling data more flexibly. In tolerant fuzzy c-means clustering, a constraint for tolerance vector which restricts the upper bound of tolerance vector is used. In this paper, regularization terms for tolerance vector are used instead of the constraint. First, the concept of clusterwise tolerance is introduced. Second, optimization problems for tolerant fuzzy c-means clustering with regularization term are formulated. Third, optimal solutions of these optimization problems are derived. Fourth, new clustering algorithms are constructed based on the explicit optimal solutions. Finally, effectiveness of proposed algorithms is verified through numerical examples.
Starting Page	197
Ending Page	202
File Size	172691
Page Count	6
File Format	PDF
ISBN	9781424448302
DOI	10.1109/GRC.2009.5255128
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty L1-regularization term Shape Clustering methods clusterwise tolerance tolerant fuzzy c-means clustering fuzzy c-means clustering Entropy L2-regularization term Data mining Constraint optimization Upper bound Clustering algorithms Machine learning
Content Type	Text
Resource Type	Article

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