关键词: 元胞自动机, 半监督聚类, K-means聚类算法, CA-K-means二阶段聚类, 复杂分布

Abstract: Semi-supervised clustering algorithm is a machine learning method which uses the priori information to improve the clustering process. Cellular automata (CA) distance transform algorithm is induced into the process of semi-supervised clustering. The dataset is divided into several clusters by distance transform of cellular automata, and then the number of clusters and the constraint information are obtained, which can be used as priori information of the next phase of clustering. In the second phase of clustering, the semi-supervised K-means clustering algorithm is used to further divide the results of the first phase and the final clustering results are got. Based on that, this paper proposes the CA-K-means clustering algorithm. By comparing the proposed algorithm with K-means algorithm, GA-K-means and pure CA clustering algorithm, the experimental results on three artificial data sets and three UCI data sets with different structures show that the novel algorithm has higher clustering accuracy for complex distributed data and more optimal clustering feature.

Key words: cellular automata, semi-supervised clustering algorithm, K-means clustering algorithm, CA-K-means two phases clustering algorithm, complex distribution