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Data clustering: a review

Authors:

P. J. FlynnAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 31, Issue 3

Pages 264 - 323

https://doi.org/10.1145/331499.331504

Published: 01 September 1999 Publication History

Abstract

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

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Reviews

Reviewer: Jose M. Ramirez

Data clustering is not defined the same way in each of the disciplines that use it to deal with problems that involve the extraction of information or structure from data. The authors have produced a good survey of this slippery topic. They devote a considerable amount of space to presenting clustering techniques from the perspective of several disciplines, including fuzzy systems, neural networks, and searching. The section of definitions and notations is weak: it is just a glossary of terms, with no context provided. It would have been better to define the terms when they were needed for each technique described. References are numerous, as expected in a survey, but are not annotated sufficiently to enable readers to define a research plan on a given aspect. In some places the paper does not reflect the state of the art in the use of clustering, as, for examples in neural networks and fuzzy systems. One cause of this weakness is the problem of dealing with a multidisciplinary subject whose advances are reported in a wide range of journals and proceedings. The other cause, namely the extremely long review process, is completely out of the authors' control: the paper was received in March 1997, but accepted in January 1999.

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Published: 01 September 1999

Published in CSUR Volume 31, Issue 3

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