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Bagging for Path-Based Clustering
November 2003 (vol. 25 no. 11)
pp. 1411-1415

Abstract—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.

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Index Terms:
Clustering, resampling, color segmentation.
Citation:
Bernd Fischer, Joachim M. Buhmann, "Bagging for Path-Based Clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 11, pp. 1411-1415, Nov. 2003, doi:10.1109/TPAMI.2003.1240115
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