An Improved Semi-Supervised K-Means Clustering Algorithm
This paper proposes an improved semi-supervised K-means clustering algorithm to deal with the data set which has a small number of labeled data.Combining with the external indexes,this algorithm determines the optimal cluster number and the initial clustering centers.The cluster effect is improved.According to the experience and the external information offered by the labeled data,this algorithm selects the maximum and minimum values of the cluster number.To each cluster number,it determines the initial clustering centers according to the labeled data and measure the clustering result.Then the optimal clustering result is confirmed.The simulation experiment shows that the algorithm in this paper has improved the cluster precision.It also has the high veracity and stability.
k-means labeled data semi-supervised clustering center
Ye Hanmin Lv Hao Sun Qianting
Guilin University of Technology Guilin,China
国际会议
重庆
英文
41-44
2016-03-20（万方平台首次上网日期，不代表论文的发表时间）