Clustering spatial data using random walks

Authors:
David Harel;Yehuda Koren
Affiliations:
The Weizmann Institute of Science, Rehovot, Israel;The Weizmann Institute of Science, Rehovot, Israel
Venue:
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2001

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Abstract

Discovering significant patterns that exist implicitly in huge spatial databases is an important computational task. A common approach to this problem is to use cluster analysis. We propose a novel approach to clustering, based on the deterministic analysis of random walks on a weighted graph generated from the data. Our approach can decompose the data into arbitrarily shaped clusters of different sizes and densities, overcoming noise and outliers that may blur the natural decomposition of the data. The method requires only O(n log n) time, and one of its variants needs only constant space.