Sequential clustering with radius and split criteria

Nenad Mladenovic (), Pierre Hansen and Jack Brimberg

Central European Journal of Operations Research, 2013, vol. 21, issue 1, 95-115

Abstract: Sequential clustering aims at determining homogeneous and/or well-separated clusters within a given set of entities, one at a time, until no more such clusters can be found. We consider a bi-criterion sequential clustering problem in which the radius of a cluster (or maximum dissimilarity between an entity chosen as center and any other entity of the cluster) is chosen as a homogeneity criterion and the split of a cluster (or minimum dissimilarity between an entity in the cluster and one outside of it) is chosen as a separation criterion. An O(N 3 ) algorithm is proposed for determining radii and splits of all efficient clusters, which leads to an O(N 4 ) algorithm for bi-criterion sequential clustering with radius and split as criteria. This algorithm is illustrated on the well known Ruspini data set. Copyright Springer-Verlag 2013

Keywords: Clustering; Sequential; Efficient cluster; Radius; Split (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s10100-012-0258-3

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