Clustering via LP-based Stabilities

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Clustering via LP-based Stabilities
A novel center-based clustering algorithm is proposed in this paper. We first formulate clustering as an NP-hard linear integer program and we then use linear programming and the duality theory to derive the solution of this optimization problem. This leads to an efficient and very general algorithm, which works in the dual domain, and can cluster data based on an arbitrary set of distances. Despite its generality, it is independent of initialization (unlike EM-like methods such as K-means), has guaranteed convergence, can automatically determine the number of clusters, and can also provide online optimality bounds about the quality of the estimated clustering solutions. To deal with the most critical issue in a centerbased clustering algorithm (selection of cluster centers), we also introduce the notion of stability of a cluster center, which is a well defined LP-based quantity that plays a key role to our algorithm's success. Furthermore, we also introduce, what we call, the ma...
Nikos Komodakis, Nikos Paragios, Georgios Tziritas
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Nikos Komodakis, Nikos Paragios, Georgios Tziritas
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