A Survey of Clustering Data Mining Techniques Export

@incollection{Berkhin2006, abstract = {Clustering is the division of data into groups of similar objects. In clustering, some details are disregarded in exchange for data simplification. Clustering can be viewed as a data modeling technique that provides for concise summaries of the data. Clustering is therefore related to many disciplines and plays an important role in a broad range of applications. The applications of clustering usually deal with large datasets and data with many attributes. Exploration of such data is a subject of data mining. This survey concentrates on clustering algorithms from a data mining perspective.}, author = {Berkhin, P.}, citeulike-article-id = {2141507}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/3-540-28349-8_2}, citeulike-linkout-1 = {http://www.springerlink.com/content/x321256p66512121}, doi = {10.1007/3-540-28349-8_2}, journal = {Grouping Multidimensional Data}, keywords = {clustering, context, d22, desktop, survey}, pages = {25--71}, posted-at = {2007-12-18 16:20:16}, priority = {2}, title = {A Survey of Clustering Data Mining Techniques}, url = {http://dx.doi.org/10.1007/3-540-28349-8_2}, year = {2006} }

TY - CHAP ID - Berkhin2006 L3 - citeulike-article-id:2141507 N2 - Clustering is the division of data into groups of similar objects. In clustering, some details are disregarded in exchange for data simplification. Clustering can be viewed as a data modeling technique that provides for concise summaries of the data. Clustering is therefore related to many disciplines and plays an important role in a broad range of applications. The applications of clustering usually deal with large datasets and data with many attributes. Exploration of such data is a subject of data mining. This survey concentrates on clustering algorithms from a data mining perspective. JF - Grouping Multidimensional Data EP - 71 TI - A Survey of Clustering Data Mining Techniques SP - 25 KW - clustering KW - context KW - d22 KW - desktop KW - survey AU - Berkhin, P PY - 2006/// UR - http://dx.doi.org/10.1007/3-540-28349-8_2 ER -