A Locality Sensitive K-Means Clustering Method Based on Genetic Algorithms

摘要：

　　The locality sensitive k-means clustering has been proposed recently.However, it performance depends greatly on the choice of the initial centers and only proper initial centers enable this clustering approach to produce a better accuracies.In this paper, an evolutionary locality sensitive k-means clustering method is presented.This new approach uses the genetic algorithms for finding its initial centers by minimizing the Davies Bouldin clustering validity index regarded as the fitness function.To investigate the effective of our approach, some experiments are done on several datasets.Experimental results show that the proposed method can get the clustering performance significantly compared to other clustering algorithms.

关键词： K-means Locality sensitive k-means Genetic Algorithms Initial centers Clustering validity index

作者: Lei Gu

作者单位: Guangxi Key Laboratory of Wireless Wideband Communication & Signal Processing, Gulin, 541004, China;School of Computer Science and Technology,Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

会议类型: 国际会议

会议名称: 4th international Conference,ICSI2013(第4届群体智能国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 114-119

在线出版日期: 2013-06-12（万方平台首次上网日期，不代表论文的发表时间）