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Article Dans Une Revue Statistics and Computing Année : 2024

Bayesian contiguity constrained clustering

Résumé

Clustering is a well-known and studied problem, one of its variants, called contiguity-constrained clustering, accepts as a second input a graph used to encode prior information about cluster structure by means of contiguity constraints i.e. clusters must form connected subgraphs of this graph. This paper discusses the interest of such a setting and proposes a new way to formalise it in a Bayesian setting, using results on spanning trees to compute exactly a posteriori probabilities of candidate partitions. An algorithmic solution is then investigated to find a maximum a posteriori partition and extract a Bayesian dendrogram from it. The interest of this last tool, which is reminiscent of the classical output of a simple hierarchical clustering algorithm, is analysed. Finally, the proposed approach is demonstrated with experiments on simulated data and real applications. A reference implementation of this work is available in the R package gtclust that accompanies the paper.
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Dates et versions

hal-04487884 , version 1 (04-03-2024)

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Etienne Côme. Bayesian contiguity constrained clustering. Statistics and Computing, 2024, 34 (2), pp.64. ⟨10.1007/s11222-023-10376-3⟩. ⟨hal-04487884⟩
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