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Bagging for path-based clustering

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2 Author(s)
B. Fischer ; Dept. of Comput. Sci. III, Rheinische Friedrich-Wilhelms-Univ., Bonn, Germany ; J. M. Buhmann

A resampling scheme for clustering with similarity to bootstrap aggregation (bagging) is presented. Bagging is used to improve the quality of path-based clustering, a data clustering method that can extract elongated structures from data in a noise robust way. The results of an agglomerative optimization method are influenced by small fluctuations of the input data. To increase the reliability of clustering solutions, a stochastic resampling method is developed to infer consensus clusters. A related reliability measure allows us to estimate the number of clusters, based on the stability of an optimized cluster solution under resampling. The quality of path-based clustering with resampling is evaluated on a large image data set of human segmentations.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:25 ,  Issue: 11 )