A randomized algorithm for estimating the number of clusters

Authors:
O. N. Granichin;D. S. Shalymov;R. Avros;Z. Volkovich
Affiliations:
St. Petersburg State University, St. Petersburg, Russia;St. Petersburg State University, St. Petersburg, Russia;ORT Braude College, Karmiel, Israel;ORT Braude College, Karmiel, Israel
Venue:
Automation and Remote Control
Year:
2011

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Abstract

Clustering is actively studied in such fields as statistics, pattern recognition, machine training, et al. A new randomized algorithm is suggested and established for finding the number of clusters in the set of data, the efficiency of which is demonstrated by examples of simulation modeling on synthetic data with thousands of clusters.