Adaptive clustering ensembles

@inproceedings{Topchy2004, abstract = {Clustering ensembles combine multiple partitions of the given data into a single clustering solution of better quality. Inspired by the success of supervised boosting algorithms, we devise an adaptive scheme for integration of multiple non-independent clusterings. Individual partitions in the ensemble are sequentially generated by clustering specially selected subsamples of the given data set. The sampling probability for each data point dynamically depends on the consistency of its previous assignments in the ensemble. New subsamples are drawn to increasingly focus on the problematic regions of the input feature space. A measure of a data point's clustering consistency is defined to guide this adaptation. An empirical study compares the performance of adaptive and regular clustering ensembles using different consensus functions on a number of data sets. Experimental results demonstrate improved accuracy for some clustering structures.}, author = {Topchy, A. and Minaei-Bidgoli, B. and Jain, A. K. and Punch, W. F. }, citeulike-article-id = {1014795}, journal = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on}, keywords = {boosting, clustering, ensemble}, pages = {272--275 Vol.1}, posted-at = {2006-12-26 15:00:14}, priority = {0}, title = {Adaptive clustering ensembles}, url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1334105}, volume = {1}, year = {2004} }

TY - CONF ID - Topchy2004 L3 - citeulike-article-id:1014795 TI - Adaptive clustering ensembles JF - Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on VL - 1 SP - 272 EP - 275 Vol.1 N2 - Clustering ensembles combine multiple partitions of the given data into a single clustering solution of better quality. Inspired by the success of supervised boosting algorithms, we devise an adaptive scheme for integration of multiple non-independent clusterings. Individual partitions in the ensemble are sequentially generated by clustering specially selected subsamples of the given data set. The sampling probability for each data point dynamically depends on the consistency of its previous assignments in the ensemble. New subsamples are drawn to increasingly focus on the problematic regions of the input feature space. A measure of a data point's clustering consistency is defined to guide this adaptation. An empirical study compares the performance of adaptive and regular clustering ensembles using different consensus functions on a number of data sets. Experimental results demonstrate improved accuracy for some clustering structures. KW - boosting KW - clustering KW - ensemble AU - Topchy, A AU - Minaei-Bidgoli, B AU - Jain, AK AU - Punch, WF PY - 2004/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1334105 ER -