Computing exact clustering posteriors with subset convolution

Jukka Kohonen and Jukka Corander

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 10, 3048-3058

Abstract: An exponential-time exact algorithm is provided for the task of clustering n items of data into k clusters. Instead of seeking one partition, posterior probabilities are computed for summary statistics: the number of clusters and pairwise co-occurrence. The method is based on subset convolution and yields the posterior distribution for the number of clusters in O(n3n) operations or O(n32n) using fast subset convolution. Pairwise co-occurrence probabilities are then obtained in O(n32n) operations. This is considerably faster than exhaustive enumeration of all partitions.

Date: 2016
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DOI: 10.1080/03610926.2014.894070

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