JACIII Vol.3 No.1 pp. 13-20
doi: 10.20965/jaciii.1999.p0013


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

July 22, 1998
September 18, 1998
February 20, 1999
Fuzzy clustering, Linear structure, Noise cluster
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.
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Last updated on Jun. 03, 2024