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