Chameleon: Hierarchical Clustering Using Dynamic Modeling

@article{citeulike:351278, abstract = {Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. Existing clustering algorithms, such as K-means, PAM, CLARANS, DBSCAN, CURE, and ROCK are designed to find clusters that fit some static models. These algorithms can breakdown if the choice of parameters in the static model is incorrect with respect to the data set being clustered, or if the model is not adequate to capture...}, author = {Karypis, George and Eui and News, Vipin K. }, citeulike-article-id = {351278}, journal = {Computer}, keywords = {clustering, hierarchy}, number = {8}, pages = {68--75}, posted-at = {2005-10-15 01:14:38}, priority = {2}, title = {Chameleon: Hierarchical Clustering Using Dynamic Modeling}, url = {http://citeseer.ist.psu.edu/karypis99chameleon.html}, volume = {32}, year = {1999} }

TY - JOUR ID - citeulike:351278 L3 - citeulike-article-id:351278 TI - Chameleon: Hierarchical Clustering Using Dynamic Modeling JF - Computer VL - 32 IS - 8 SP - 68 EP - 75 N2 - Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. Existing clustering algorithms, such as K-means, PAM, CLARANS, DBSCAN, CURE, and ROCK are designed to find clusters that fit some static models. These algorithms can breakdown if the choice of parameters in the static model is incorrect with respect to the data set being clustered, or if the model is not adequate to capture... KW - clustering KW - hierarchy AU - Karypis, George AU - Eui AU - News, Vipin PY - 1999/// UR - http://citeseer.ist.psu.edu/karypis99chameleon.html ER -