DocumentCode :
2786623
Title :
Algorithms for k-means clustering problem with balancing constraint
Author :
Shouqiang, Wang ; Zengxiao, Chi ; Sheng, Zhan
Author_Institution :
Dept. of Inf. Eng., Shandong Jiaotong Univ., Jinan, China
fYear :
2009
fDate :
17-19 June 2009
Firstpage :
3967
Lastpage :
3972
Abstract :
k-means clustering has been widely applied in the field of Machine Learning and Pattern Recognition. This paper discussed the algorithm of its sub problem which requires that each divided subset size must have at least some given value. Firstly, given k centers, this paper presented an algorithm that assigned each point to one of the centers and proved that the solution value is minimized. Secondly, a 2-approximate algorithm is also presented by the sample technique. At last some UCI datasets were selected to verify our algorithm.
Keywords :
approximation theory; learning (artificial intelligence); pattern clustering; 2-approximate algorithm; K-means clustering problem algorithms; balancing constraint; machine learning; pattern recognition; Clustering algorithms; Machine learning; Machine learning algorithms; Pattern recognition; Algorithm; Balancing Constraint; Clustering; k-means;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-2722-2
Electronic_ISBN :
978-1-4244-2723-9
Type :
conf
DOI :
10.1109/CCDC.2009.5192108
Filename :
5192108
Link To Document :
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