Unsupervised and semi-supervised clustering by message passing: Soft-constraint affinity propagation

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dc.creatorLeone, Michele
dc.creatorSumedha
dc.creatorWeigt, Martin
dc.date2007-12-07
dc.date2008-09-15
dc.date.accessioned2026-07-07T10:11:08Z
dc.date.available2026-07-07T10:11:08Z
dc.descriptionSoft-constraint affinity propagation (SCAP) is a new statistical-physics based clustering technique. First we give the derivation of a simplified version of the algorithm and discuss possibilities of time- and memory-efficient implementations. Later we give a detailed analysis of the performance of SCAP on artificial data, showing that the algorithm efficiently unveils clustered and hierarchical data structures. We generalize the algorithm to the problem of semi-supervised clustering, where data are already partially labeled, and clustering assigns labels to previously unlabeled points. SCAP uses both the geometrical organization of the data and the available labels assigned to few points in a computationally efficient way, as is shown on artificial and biological benchmark data.
dc.description11 pages, 13 pdf figures, to app. in EPJB
dc.identifierhttps://arxiv.org/abs/0712.1165
dc.identifierhttp://arxiv.org/abs/0712.1165
dc.identifierEur. Phys. J. B (2008), published online 8 Oct. 2008
dc.identifierdoi:10.1140/epjb/e2008-00381-8
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/171770
dc.subjectData Analysis, Statistics and Probability
dc.subjectStatistical Mechanics
dc.subjectQuantitative Methods
dc.titleUnsupervised and semi-supervised clustering by message passing: Soft-constraint affinity propagation
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