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Finding the number of clusters in ordered dissimilarities

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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.

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Acknowledgments

This work was funded by the National Science Foundation under ITR grant number IIS-0428420. The authors would also like to thank the reviewers for their insightful comments that helped to improve the quality of this manuscript.

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, University of Missouri, Columbia, MO, USA

    Isaac J. Sledge, Timothy C. Havens & James M. Keller

  2. Computer Science Department, University of West Florida, Pensacola, FL, USA

    Jacalyn M. Huband & James C. Bezdek

Authors
  1. Isaac J. Sledge
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  2. Timothy C. Havens
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  3. Jacalyn M. Huband
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  4. James C. Bezdek
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  5. James M. Keller
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Correspondence to Isaac J. Sledge.

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Sledge, I.J., Havens, T.C., Huband, J.M. et al. Finding the number of clusters in ordered dissimilarities. Soft Comput 13, 1125–1142 (2009). https://doi.org/10.1007/s00500-009-0421-5

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