Clustering With The Relational C-Means Algorithms Using Different Measures Of Pairwise Distance

Richard J. Hathaway; John W. Davenport; James C. Bezdek

doi:10.1117/12.976609

22 August 1988 Clustering With The Relational C-Means Algorithms Using Different Measures Of Pairwise Distance

Richard J. Hathaway, John W. Davenport, James C. Bezdek

Proceedings Volume 0938, Digital and Optical Shape Representation and Pattern Recognition; (1988) https://doi.org/10.1117/12.976609
Event: 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, 1988, Orlando, FL, United States

Abstract

In this note we review the object and relational c-means algorithms, and the theory asserting their duality in case the relational data corresponds to an inner-product induced measure of distance between each pair of corresponding object data. Past numerical results are given here along with new extensions in order to study the effect of the choice of pairwise distance measure on the relational partition obtained.

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Richard J. Hathaway, John W. Davenport, and James C. Bezdek "Clustering With The Relational C-Means Algorithms Using Different Measures Of Pairwise Distance", Proc. SPIE 0938, Digital and Optical Shape Representation and Pattern Recognition, (22 August 1988); https://doi.org/10.1117/12.976609

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