document: Clustering spatial data using random walks 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 .
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