中文摘要：

基于传统吸引子传播算法,通过样本特征赋权,克服冗余信息的影响及给出新的相似性度量方法等策略,提出一种基于变异系数赋权的吸引子传播算法.实验结果表明,该算法在处理属性较多、信息重叠的样本时,不仅具有吸引子传播算法的快速、高效聚类特征,且聚类性能明显优于传统吸引子传播算法和K-均值等经典聚类算法.

英文摘要：

An improved affinity propagation algorithm based on coefficient of variation was proposed via feature weighing of samples,which has overcome the impact of redundant information,and the new similarity measure method was proposed.The experimental results show that the proposed algorithm is not only quick and efficient but also better than the traditional affinity propagation algorithm and the classical K-means method for clustering when it was used to process the samples with more characteristics and attributes,and information overlap.