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.