Survey Of Clustering Data Mining Techniques

@techreport{citeulike:556827, abstract = {Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters neccessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historial perspective rooted in mathematics, statistics and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters in unsupervised learning and the resulting system represents a data concept....}, address = {San Jose, CA}, author = {Berkhin, Pavel }, citeulike-article-id = {556827}, institution = {Accrue Software}, keywords = {clustering}, posted-at = {2006-03-20 16:40:16}, priority = {2}, title = {Survey Of Clustering Data Mining Techniques}, url = {http://citeseer.ist.psu.edu/berkhin02survey.html}, year = {2002} }

TY - RPRT ID - citeulike:556827 L3 - citeulike-article-id:556827 TI - Survey Of Clustering Data Mining Techniques AD - San Jose, CA N2 - Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters neccessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historial perspective rooted in mathematics, statistics and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters in unsupervised learning and the resulting system represents a data concept.... KW - clustering AU - Berkhin, Pavel PY - 2002/// UR - http://citeseer.ist.psu.edu/berkhin02survey.html ER -