DocumentCode :
2341022
Title :
A novel algorithm for initializing clustering centers
Author :
Yang, Shu-Zhong ; Luo, Si-Wei
Author_Institution :
Dept. of Comput. Sci., Beijing Jiaotong Univ., China
Volume :
9
fYear :
2005
fDate :
18-21 Aug. 2005
Firstpage :
5579
Abstract :
It is known that many clustering algorithms which converge to one of numerous local minima through an iterative procedure are especially sensitive to initial clustering centers. In this paper we propose a novel algorithm for refining initial clustering centers. In the algorithm we define two new measurements to measure a point\´s local density and then produce a clustering center with local maximal density for each cluster using either of measurements. After refinement, these clustering algorithms which are sensitive to initial clustering centers will converge to a "better" local minimum more efficiently and more rapidly. Experiments demonstrate that the proposed algorithm is feasible and efficient.
Keywords :
iterative methods; pattern clustering; random processes; sampling methods; clustering algorithm; clustering centers; iterative procedure; k-density; k-means clustering; local density; local minima; z-density; Clustering algorithms; Computer science; Data analysis; Data mining; Density measurement; Gaussian processes; Iterative algorithms; Optimization methods; Sampling methods; Vector quantization; K-Means; c-density; dustering centers; initialization; k-density;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
Type :
conf
DOI :
10.1109/ICMLC.2005.1527930
Filename :
1527930
Link To Document :
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