A K-means Algorithm Based On Feature Weighting
Cluster analysis is a statistical analysis technique that divides the research objects into relatively homogeneous groups.The core of cluster analysis is to find useful clusters of objects.K-means clustering algorithm has been receiving much attention from scholars because of its excellent speed and good scalability.However,the traditional K-means algorithm does not consider the influence of each attribute on the final clustering result,which makes the accuracy of clustering have a certain impact.In response to the above problems,this paper proposes an improved feature weighting algorithm.The improved algorithm uses the information gain and ReliefF feature selection algorithm to weight the features and correct the distance function between clustering objects,so that the algorithm can achieve more accurate and efficient clustering effect.The simulation results show that compared with the traditional K-means algorithm,the improved algorithm clustering results are stable,and the accuracy of clustering is significantly improved..
Yan Xu Xueliang Fu Honghui Li Gaifang Dong Qing Wang
College of Computer and Information Engineering,Inner Mongolia Agricultural University,Hohhot,Inner Mongolia 010020,China
国际会议
上海
英文
1-5
2018-10-12（万方平台首次上网日期，不代表论文的发表时间）