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Proceedings Paper

New fuzzy shell clustering algorithms for boundary detection and pattern recognition
Author(s): Raghu J. Krishnapuram; Hichem Frigui; Olfa Nasraoui
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Paper Abstract

In this paper, we introduce new hard and fuzzy clustering algorithms called the c-quadric shells (CQS) algorithms. These algorithms are specifically designed to seek clusters that can be described by segments of second-degree curves, or more generally by segments of shells of hyperquadrics. Previous shell clustering algorithms have considered clusters of specific shapes such as circles (the fuzzy c-shells algorithm) or ellipses (the fuzzy c-ellipsoids algorithm). The advantage of our algorithm lies in the fact that it can be used to cluster mixtures of all types of hyperquadrics such as hyperspheres, hyperellipsoids, hyperparaboloids, hyperhyperboloids, and even hyperplanes. Several examples of clustering in the two-dimensional case are shown.

Paper Details

Date Published: 1 February 1992
PDF: 8 pages
Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57082
Show Author Affiliations
Raghu J. Krishnapuram, Univ. of Missouri/Columbia (United States)
Hichem Frigui, Univ. of Missouri/Columbia (United States)
Olfa Nasraoui, Univ. of Missouri/Columbia (United States)


Published in SPIE Proceedings Vol. 1607:
Intelligent Robots and Computer Vision X: Algorithms and Techniques
David P. Casasent, Editor(s)

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