关键词: 聚类分析, k-均值算法, prim算法, 初始化敏感, 聚类中心

Abstract: According to the initialization sensitivity problem of the traditional k-means algorithm, a heuristic initialization-
independent k-means algorithm is proposed. Prim algorithm is introduced to solve the selection of initial clustering centers, and the threshold parameter θ is set, which can avoid several data objects from the same class as the initial clustering centers simultaneously, otherwise the algorithm increases the iteration times, and the wrong clustering results are got. Compared with the traditional k-means algorithm and k-means clustering analysis based on genetic algorithm, the experimental result shows that the improved algorithm not only reduces the impact of random selection of initial clustering centers, and decreases the iteration times effectively, but also reduces the affect of outliers in the process of clustering, which validates the feasibility and effectiveness of the suggested algorithm.

Key words: clustering analysis, k-means clustering, prim algorithm, initialization sensitivity, clustering center