Feature Weighting in k-Means Clustering Export

@inproceedings{Modha2002Data, abstract = {Data sets with multiple, heterogeneous feature spaces occur frequently. We present an abstract framework for integrating multiple feature spaces in the k-means clustering algorithm. Our main ideas are (i) to represent each data object as a tuple of multiple feature vectors, (ii) to assign a suitable (and possibly different) distortion measure to each feature space, (iii) to combine distortions on different feature spaces, in a convex fashion, by assigning (possibly) different relative weights to each, (iv) for a fixed weighting, to cluster using the proposed convex k-means algorithm, and (v) to determine the optimal feature weighting to be the one that yields the clustering that simultaneously minimizes the average within-cluster dispersion and maximizes the average between-cluster dispersion along all the feature spaces. Using precision/recall evaluations and known ground truth classifications, we empirically demonstrate the effectiveness of feature weighting in clustering on several different application domains.}, author = {Modha, Dharmendra and Spangler, Scott}, booktitle = {Machine Learning}, citeulike-article-id = {6095048}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.5686}, keywords = {clustering, machine\_learning}, posted-at = {2009-11-10 15:03:57}, priority = {2}, title = {Feature Weighting in k-Means Clustering}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.5686}, volume = {52}, year = {2002} }

TY - CONF ID - Modha2002Data L3 - citeulike-article-id:6095048 N2 - Data sets with multiple, heterogeneous feature spaces occur frequently. We present an abstract framework for integrating multiple feature spaces in the k-means clustering algorithm. Our main ideas are (i) to represent each data object as a tuple of multiple feature vectors, (ii) to assign a suitable (and possibly different) distortion measure to each feature space, (iii) to combine distortions on different feature spaces, in a convex fashion, by assigning (possibly) different relative weights to each, (iv) for a fixed weighting, to cluster using the proposed convex k-means algorithm, and (v) to determine the optimal feature weighting to be the one that yields the clustering that simultaneously minimizes the average within-cluster dispersion and maximizes the average between-cluster dispersion along all the feature spaces. Using precision/recall evaluations and known ground truth classifications, we empirically demonstrate the effectiveness of feature weighting in clustering on several different application domains. TI - Feature Weighting in k-Means Clustering VL - 52 T2 - Machine Learning KW - clustering KW - machine_learning AU - Modha, Dharmendra AU - Spangler, Scott PY - 2002/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.5686 ER -