Global Optimization for Semi-supervised K-means

So far most of the K-means algorithms use the number of the labeled data as the K value, but sometimes it doesnpsilat work well. In this paper, we propose a semi-supervised K-means algorithm based on the global optimization. It can select an appropriate number of clusters as the K value directly and plan a great amount of supervision data by using only a small amount of the labeled data. Combining the distribution characteristics of data sets and monitoring information in each cluster after clustering, we use the voting rule to guide the cluster labeling in the data sets. The experiments indicated that the global optimization algorithm for semi-supervised K-means is quite helpful to improve the K-means algorithm, it can effectively find the best data sets for K values and clustering center and enhancing the performance of clustering.