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.
%0 Book Section
%1 Berkhin2006
%A Berkhin, P.
%D 2006
%J Grouping Multidimensional Data
%K clustering context d22 desktop survey
%P 25--71
%R http://dx.doi.org/10.1007/3-540-28349-8\_2
%T A Survey of Clustering Data Mining Techniques
%U http://dx.doi.org/10.1007/3-540-28349-8\_2
%X 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.
@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.},
added-at = {2009-03-12T15:42:50.000+0100},
author = {Berkhin, P.},
biburl = {https://www.bibsonomy.org/bibtex/2cf6693aa2f197b43552a449e28e2b2a0/lillejul},
citeulike-article-id = {2141507},
doi = {http://dx.doi.org/10.1007/3-540-28349-8\_2},
interhash = {fc69b0651d1395d91e6cdcbcf80bcd7b},
intrahash = {cf6693aa2f197b43552a449e28e2b2a0},
journal = {Grouping Multidimensional Data},
keywords = {clustering context d22 desktop survey},
pages = {25--71},
posted-at = {2007-12-18 16:20:16},
priority = {2},
timestamp = {2009-03-12T15:42:51.000+0100},
title = {A Survey of Clustering Data Mining Techniques},
url = {http://dx.doi.org/10.1007/3-540-28349-8\_2},
year = 2006
}