基于密集度量元的近邻传播聚类算法

Algorithm of Affinity Propagation Clustering Based on Density Similarity Measurement

聚类是数据挖掘领域中发现数据隐含模式的有效方法之一.针对传统近邻传播聚类算法中采用欧式距离表示数据相似度,不能有效处理复杂结构数据的不足,提出了一种基于密集度量元的近邻传播聚类算法.该算法首先引入密度的思想,然后在近邻传播算法的框架下定义密度因子,设计新的空间一致性距离测度类欧式距离,构造基于密度敏感的相似性度量元,提高了传统算法处理复杂结构数据的性能.最后通过仿真实验验证了该算法的有效性.

Clustering is an effective method for discovering the potential information in the fields of data mining.Aiming at the problem of traditional affinity propagation(AP)clustering algorithm denoted by Euclidean measure can not deal with the complicated data sets,a novel algorithm,affinity propagation with density similarity measurement(APDSM),is presented.Firstly,the idea of density is introduced.Then under the frame of traditional affinity propagation,the density gene is defined and novel similar-Euclidean measure is designed.A density sensitive similarity measurement is constructed as well.Finally experiment is used to validate the algorithm.