Semi-supervised learning based on K-means clustering algorithm
基于半监督学习的K-均值聚类算法研究

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.