关键词:半监督聚类; 改进的K-均值算法; 动态管理种群的粒子群算法
Semi-supervised learning based on K-means clustering algorithm
LIU Tao1, YIN Hong-jian2
(1.Dept. of Information Technology, Zhengzhou Teachers College, Zhengzhou 450044, China; 2.Computing Center of Information,Hunan Vocational College of Chemical, Zhuzhou Hunan 412004, China)
Abstract:This paper constructed a new classified function which mixed Euclidean distance with supervising information. Taking into account that K-means algorithm was sensitive to the initial center, used search space of particle swarm algorithm was used to simulate the clustering Euclidean space to find a better cluster center of clustering. At the same time, brought up a strategy of species dynamic management to improve the efficiency of particle swarm optimization search. The algorithm got a good clustering accuracy on a number of UCI testing data sets. ......