Affiliation:
1. SISSA (Scuola Internazionale Superiore di Studi Avanzati), via Bonomea 265, I-34136 Trieste, Italy.
Abstract
Discerning clusters of data points
Cluster analysis is used in many disciplines to group objects according to a defined measure of distance. Numerous algorithms exist, some based on the analysis of the local density of data points, and others on predefined probability distributions. Rodriguez and Laio devised a method in which the cluster centers are recognized as local density maxima that are far away from any points of higher density. The algorithm depends only on the relative densities rather than their absolute values. The authors tested the method on a series of data sets, and its performance compared favorably to that of established techniques.
Science
, this issue p.
1492
Publisher
American Association for the Advancement of Science (AAAS)
Reference22 articles.
1. Survey of Clustering Algorithms
2. J. MacQueen in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability L. M. Le Cam J. Neyman Eds. (Univ. California Press Berkeley CA 1967) vol. 1 pp. 281–297.
3. L. Kaufman P. J. Rousseeuw Finding Groups in Data: An Introduction to Cluster Analysis vol. 344 (Wiley-Interscience New York 2009).
4. Clustering by Passing Messages Between Data Points
5. Hierarchical Grouping to Optimize an Objective Function
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