DocumentCode :
1863478
Title :
An enhanced k-means algorithm using agglomerative hierarchical clustering strategy
Author :
Jianjun Cheng ; Xiaoyun Chen ; Haijuan Yang ; Mingwei Leng
Author_Institution :
School of Information Science & Engineering, Lanzhou University, Gansu Province, China
fYear :
2012
fDate :
3-5 March 2012
Firstpage :
407
Lastpage :
410
Abstract :
To overcome the drawback that the k-means algorithm is sensitive to the selection of initial centroids, we proposed an enhanced two-stage k-means algorithm. In the first stage, we begin with selecting as many as enough initial centroids, then the basic k-means algorithm is applied to get the intermediate clusters, i.e., we keep the number of initial centroids k′ large enough to eliminate the bad centroids´ effect to the result. In the second stage, the k′ intermediate clusters are merged into k result clusters using agglomerative hierarchical clustering algorithm. We have tested our algorithm on standard data sets and synthesized data set; experiments results have manifested that our algorithm can obtain higher clustering accuracy.
Keywords :
Centroids; Clustering; then k-means algorithm;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Automatic Control and Artificial Intelligence (ACAI 2012), International Conference on
Conference_Location :
Xiamen
Electronic_ISBN :
978-1-84919-537-9
Type :
conf
DOI :
10.1049/cp.2012.1003
Filename :
6492610
Link To Document :
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