Criteria for polynomial-time (conceptual) clustering

Research in cluster analysis has resulted in a large number of algorithms and similarity measurements for clustering scientific data. Machine learning researchers have published a number of methods for conceptual clustering, in which observations are grouped into clusters that have "good"...

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Bibliographic Details
Published in:Machine learning 1988-04, Vol.2 (4), p.371-396
Main Authors: Pitt, Leonard, Reinke, Robert E.
Format: Article
Language:English
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Summary:Research in cluster analysis has resulted in a large number of algorithms and similarity measurements for clustering scientific data. Machine learning researchers have published a number of methods for conceptual clustering, in which observations are grouped into clusters that have "good" descriptions in some language. In this paper the authors investigate the general properties that similarity metrics, objective functions, and concept description languages must have to guarantee that a (conceptual) clustering problem is polynomial-time solvable by a simple and widely used clustering technique, the agglomerative-hierarchical algorithm.
ISSN:0885-6125
1573-0565
DOI:10.1007/BF00116830