Publications

KDNuggets subscribers may be interested in the following
paper in ACM Computing Surveys.  Although the paper appears
in the issue dated September 1999, that issue was not
published until recently.

A. K. Jain, M. N. Murty and P. J. Flynn,
Data clustering: a review,
ACM Computing Surveys, v. 31, n. 3, September 1999,
pp. 264-323.

http://www.acm.org/pubs/contents/journals/surveys/1999-31/#3

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.

--
Patrick J. Flynn (flynn.84@osu.edu) ss
Associate Professor of Electrical Engineering               s
The Ohio State University                                 c[_]
614-688-8225 phone; 614-292-7596 fax