Fuzzy hyper-prototype clustering

Authors:
Jin Liu;Tuan D. Pham
Affiliations:
School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia;School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia
Venue:
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Year:
2010

Citing 10
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Arbitrary-norm separating planefn1fn1This material is based on research supported by NSF Grant CCR-9322479 and AFOSR Grant F49620-97-1-0326.

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A spatially constrained fuzzy hyper-prototype clustering algorithm

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Abstract

We propose a fuzzy hyper-prototype algorithm in this paper. This approach uses hyperplanes to represent the cluster centers in the fuzzy c-means algorithm. We present the formulation of a hyperplane-based fuzzy objective function and then derive an iterative numerical procedure for minimizing the clustering criterion. We tested the method with data degraded with random noise. The experimental results show that the proposed method is robust to clustering noisy linear structure.