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Proceedings Paper

Least-biased fuzzy clustering method for inhomogeneous data
Author(s): Gerardo Beni; Xiaomin Liu
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Paper Abstract

We have extended the least biased fuzzy clustering algorithm to inhomogeneous data sets. The resolution parameter is generalized from a scalar to a vector with the dimension of the feature space. We fix the orientation of the resolution vector to measure the relative inhomogeneities of each cluster of data points in the different dimensions; and we study the effect of the magnitude of the resolution parameter on the phase transitions yielding the clusters. Based on the detection of the onset of a phase transition, a new technique for truncating the iteration scheme of solution reduces the computational complexity to the order of the number of data points. The actual computational load of the algorithm is discussed and examples are given to illustrate the performance of the algorithm in clustering inhomogeneous data sets.

Paper Details

Date Published: 13 June 1995
PDF: 8 pages
Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, (13 June 1995); doi: 10.1117/12.211791
Show Author Affiliations
Gerardo Beni, Univ. of California/Riverside (United States)
Xiaomin Liu, Univ. of California/Riverside (United States)


Published in SPIE Proceedings Vol. 2493:
Applications of Fuzzy Logic Technology II
Bruno Bosacchi; James C. Bezdek, Editor(s)

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