Physics – Data Analysis – Statistics and Probability
Scientific paper
2007-12-07
Eur. Phys. J. B (2008), published online 8 Oct. 2008
Physics
Data Analysis, Statistics and Probability
11 pages, 13 pdf figures, to app. in EPJB
Scientific paper
10.1140/epjb/e2008-00381-8
Soft-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.
Leone Michele
Sumedha
Weigt Martin
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