Finding the number of clusters in ordered dissimilarities

  • Authors:

  • Isaac J. Sledge;Timothy C. Havens;Jacalyn M. Huband;James C. Bezdek;James M. Keller

  • Affiliations:

  • University of Missouri, Electrical and Computer Engineering Department, Columbia, MO, USA;University of Missouri, Electrical and Computer Engineering Department, Columbia, MO, USA;University of West Florida, Computer Science Department, Pensacola, FL, USA;University of West Florida, Computer Science Department, Pensacola, FL, USA;University of Missouri, Electrical and Computer Engineering Department, Columbia, MO, USA

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on ICNC-FSKD’2008;Guest Editors: Liang Zhao, Maozu Guo, Lipo Wang
  • Year:
  • 2009

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Abstract

As humans, we have innate faculties that allow us to efficiently segment groups of objects. Computers, to some degree, can be programmed with similar categorical capabilities, which stem from exploratory data analysis. Out of the various subsets of data reasoning, clustering provides insight into the structure and relationships of input samples situated in a number of distributions. To determine these relationships, many clustering methods rely on one or more human inputs; the most important being the number of distributions, c, to seek. This work investigates a technique for estimating the number of clusters from a general type of data called relational data. Several numerical examples are presented to illustrate the effectiveness of the proposed method.