Issue No. 11 - November (2003 vol. 25)
Bernd Fischer , IEEE
Joachim M. Buhmann , IEEE
<p><b>Abstract</b>—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. </p>
Clustering, resampling, color segmentation.
J. M. Buhmann and B. Fischer, "Bagging for Path-Based Clustering," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 25, no. , pp. 1411-1415, 2003.