Title :
Improved algorithm for the k-means clustering
Author :
Zhang, Sheng ; Wang, Shouqiang
Author_Institution :
Sch. of Inf. Sci. & Electr. Eng., Shandong Jiaotong Univ., Jinan, China
Abstract :
This paper investigates the standard k-means clustering and gives an improved algorithm based on selecting the initial centers and overcoming the local minimal values. Experiments show that the new algorithm is more effective and can get a better result than the standard k-means clustering.
Keywords :
pattern clustering; improved algorithm; initial center selection; local minimal values; standard k-means clustering; Approximation methods; Clustering algorithms; Educational institutions; Electrical engineering; Information science; Lead; Standards; clustering; clustering center; k-means;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2012 10th World Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-1397-1
DOI :
10.1109/WCICA.2012.6359372
