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Fuzzy Clustering for Detecting Linear Structures with Different Dimensions
Kazutaka Umayahara*, Yoshiteru Nakamori** and Sadaaki Miyamoto*
*Institute of Information Sciences and Electronics University of Tsukuba Tsukuba, Ibaraki 305-8573, Japan
**Graduate School of Knowledge Science Japan Advanced Institute of Science and Technology, Hokuriku Tatsunokuchi, Ishikawa 923-1292, Japan
Received:July 22, 1998Accepted:September 18, 1998Published:February 20, 1999
Keywords:Fuzzy clustering, Linear structure, Noise cluster
Abstract
One recent interest in fuzzy clustering is the simultaneous determination of a fuzzy partition of a given dataset and parameters of assumed models having different shapes that explain partitioned datasets. We propose an objective function to detect linear varieties with different dimensionalities. The noise cluster suggested by Dave is introduced. Since this is not all-purpose method, some techniques are suggested using artificial examples to show how to implement clustering successfully.
Cite this article as:K. Umayahara, Y. Nakamori, and S. Miyamoto, “Fuzzy Clustering for Detecting Linear Structures with Different Dimensions,” J. Adv. Comput. Intell. Intell. Inform., Vol.3 No.1, pp. 13-20, 1999.Data files:
