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Toward the Intelligent Control of Hierarchical Clustering

  • Ronald R. Yager
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 89)

Abstract

The importance of the inter-cluster distance measure in the determination of the clusters in hierarchical clustering is pointed out. We note a fundamental distinction between the nearest neighbor cluster distance measure, Min, and the furthest neighbor measure, Max. The first favors the merging of large clusters while the later favors the merging of smaller clusters. We introduce a family of inter-cluster distance measures which can be parameterized along a scale characterizing their preference for merging larger or smaller clusters. We then consider the use of this distinction between distance measures as a way of controlling the hierarchical clustering process. Combining this with the ability of fuzzy systems modeling to formalize linguistic specifications we see the emergence of a tool that can aid in the inclusion of intelligence in the clustering process.

Keywords

Distance Measure Small Cluster Cluster Process Fuzzy Subset Aggregation Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Ronald R. Yager
    • 1
  1. 1.Machine Intelligence InstituteInstitute Iona CollegeNew RochelleUSA

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