Semi-supervised learning based on K-means clustering algorithm
Hongjian Yin · Jisuanji yingyong yanjiu · 2010
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.