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Proceedings Paper

Modified FCM clustering based on kernel mapping
Author(s): Zeyu Li; Shiwei Tang; Jing Xue; Jun Jiang
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Paper Abstract

A modified method for performing nonlinear form of Fuzzy C-Means (FCM) clustering algorithm (K-FCM) is proposed. By the use of kernel mapping, the non-linear clustering problem can be efficiently transformed into a linear problem in high-dimensional, even infinite, feature space. At the same time, we need not to know the explicit form of the non-linear mapping. That means that the computational complexity will not raised largely. The experimental result reveals the efficient and effective of the method proposed in this paper.

Paper Details

Date Published: 24 September 2001
PDF: 5 pages
Proc. SPIE 4554, Object Detection, Classification, and Tracking Technologies, (24 September 2001); doi: 10.1117/12.441658
Show Author Affiliations
Zeyu Li, Peking Univ. (China)
Shiwei Tang, Peking Univ. (China)
Jing Xue, Peking Univ. (China)
Jun Jiang, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 4554:
Object Detection, Classification, and Tracking Technologies
Jun Shen; Sharatchandra Pankanti; Runsheng Wang, Editor(s)

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