基于互信息和分形维数相结合的选择性聚类融合算法研究

Research on Selective Clustering Ensemble Algorithm Based on Normalized Mutual Information and Fractal Dimension

针对传统聚类融合算法不能消除劣质聚类成员的干扰,以及聚类准确性不高等问题,提出一种基于分形维数的选择性聚类融合算法.该算法实现增量式聚类,能够发现任意形状的聚类.通过基于互信息计算权值的选择策略,选取部分优质聚类成员,再利用加权共协矩阵实现融合,获得最终的聚类结果.实验证明,与传统聚类融合算法相比,该算法提高了聚类质量,具有较好的扩展性.

Traditional clustering ensemble algorithm can not eliminate the influence of inferior quality clustering members and is also characterized with lower clustering accuracy. To solve the problems,a selective clustering ensemble algorithm based on fractal dimension is proposed. Firstly,the proposed algorithm is used to realize incremental clustering and can find arbitrary shape clustering. Then,according to the selection strategy of weight values based on normalized mutual information,the proposed algorithm selects high quality clustering members to realize integration by using weighted co-association matrix and get the final clustering results. The experimental results show that compared to the traditional clustering ensemble algorithm,the proposed algorithm improves the clustering quality and has good extensibility.