STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Fast hierarchical clustering and other applications of dynamic closest pairs
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Algorithms and theory of computation handbook
The three steps of clustering in the post-genomic era: a synopsis
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
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Microarrays are used for measuring expression levels of thousands of genes simultaneously. Clustering algorithms are used on gene expression data to find co-regulated genes. An often used clustering strategy is the Pearson correlation coefficient based hierarchical clustering algorithm presented in [Proc. Nat. Acad. Sci. 95 (25) (1998) 14863-14868], which takes O(N^3) time. We note that this run time can be reduced to O(N^2) by applying known hierarchical clustering algorithms [Proc. 9th Annual ACM-SIAM Symposium on Discrete Algorithms, 1998, pp. 619-628] to this problem. In this paper, we present an algorithm which runs in O(NlogN) time using a geometrical reduction and show that it is optimal.