DocumentCode :
2045189
Title :
A Hierarchical Clustering Algorithm for Categorical Attributes
Author :
Agarwal, Parul ; Alam, M. Afshar ; Biswas, Ranjit
Author_Institution :
Dept. of Comput. Sci., Jamia Hamdard, New Delhi, India
Volume :
2
fYear :
2010
fDate :
19-21 March 2010
Firstpage :
365
Lastpage :
368
Abstract :
Clustering, an important technique of data mining, groups similar objects together and identifies the cluster number to which each object of the domain being studied belongs to. In this paper we propose a clustering algorithm which produces quite accurate clusters using the bottom up approach of hierarchical clustering technique of data with categorical attributes. A similarity measure has been proposed on the basis of which merge operations are carried out untill the desired number of clusters are obtained.
Keywords :
data mining; pattern clustering; categorical attributes; data mining; hierarchical clustering algorithm; similar objects grouping; similarity measure; Application software; Clustering algorithms; Computer applications; Computer science; Conference management; Data engineering; Data mining; Information technology; Merging; Partitioning algorithms; bottom up hierarchical clustering; categorical attributes; similarity measure;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Engineering and Applications (ICCEA), 2010 Second International Conference on
Conference_Location :
Bali Island
Print_ISBN :
978-1-4244-6079-3
Electronic_ISBN :
978-1-4244-6080-9
Type :
conf
DOI :
10.1109/ICCEA.2010.222
Filename :
5445672
Link To Document :
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