DocumentCode :
3759435
Title :
Estimation of Clusters Number and Initial Centers of K-Means Algorithm Using Watershed Method
Author :
Xiaolong Wang;Yiping Jiao;Shumin Fei
Author_Institution :
Sch. of Autom., Southeast Univ., Nanjing, China
fYear :
2015
Firstpage :
505
Lastpage :
508
Abstract :
In K-means clustering algorithm, the selection of cluster number k and initial K-means center has certain influence on the result. It would generate very different aggregation result when confronting with some certain types of data set. This paper aims at proposing an estimation method to evaluate the initial parameters for K-means algorithm. The estimation is executed through data analysis, which contains two main steps: the data would be transformed into data dimensional density first, and then, watershed method would be applied to divide the data space into multiple regions. Each regional center is selected as an initial K-means center, and the number of region is set as cluster number. This estimation method takes advantage of image segmentation ideology and the case study in this paper showed its favorable performance.
Keywords :
"Clustering algorithms","Estimation","Classification algorithms","Parameter estimation","Algorithm design and analysis","Kernel","Distributed computing"
Publisher :
ieee
Conference_Titel :
Distributed Computing and Applications for Business Engineering and Science (DCABES), 2015 14th International Symposium on
Type :
conf
DOI :
10.1109/DCABES.2015.132
Filename :
7429666
Link To Document :
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