Title :
A new method on finding optimal centers for improving K-means algorithm
Author :
Jie Zhang ; Jianrui Dong ; Yiyong Xiao
Author_Institution :
Sch. of Reliability & Syst. Eng., Beihang Univ., Beijing, China
Abstract :
The mean center (geometric center) has always been used to represent the cluster center in classical K-means algorithm which may cause error. In this paper, a new method, P-partition Method, is introduced to obtain the center of a cluster, which proved efficient and globally optimal. Then two related clustering algorithms are presented by replacing the mean center of K-means based on the new centers found by P-partition, both of which are verified in experiments to be able to provide a better objective value (averagely about 3% lower than that of K-means) under the same conditions.
Keywords :
data mining; geometry; pattern clustering; K-means algorithm; P-partition method; clustering algorithms; geometric center; mean center; optimal center finding method; Algorithm design and analysis; Clustering algorithms; Computational efficiency; Cost function; Linear programming; Partitioning algorithms; K-means; P-partition; clustering algorithm; partition;
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
DOI :
10.1109/CCDC.2015.7162216