Automatically finding clusters in normalized cuts

Abstract :

Normalized Cuts is a state-of-the-art spectral method for clustering. By applying spectral techniques, the data becomes easier to cluster and then k-means is classically used. Unfortunately the number of clusters must be manually set and it is very sensitive to initialization. Moreover, k-means tends to split large clusters, to merge small clusters, and to favor convex-shaped clusters. In this work we present a new clustering method which is parameterless, independent from the original data dimensionality and from the shape of the clusters. It only takes into account inter-point distances and it has no random steps. The combination of the proposed method with normalized cuts proved successful in our experiments.

Type de document :
Article dans une revue
Pattern Recognition, Elsevier, 2011, 44 (7), pp.1372-1386. 〈10.1016/j.patcog.2011.01.003〉
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Mariano Tepper, Pablo Musé, A. Almansa, Marta Mejail. Automatically finding clusters in normalized cuts. Pattern Recognition, Elsevier, 2011, 44 (7), pp.1372-1386. 〈10.1016/j.patcog.2011.01.003〉. 〈hal-00631620〉

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