带信息反馈的凝聚层次聚类算法

Abstract

The paper proposes an hierarchical agglomerative clustering with information feedback, aiming to solve the problems (the absence of direction information in similarity measurement and relatively poor adaptability) in traditional hierarchical clustering algorithms based on similarity. In the algorithm, the unpredictable complicated data structure is described with three basic structural feature units, and then it is modeled to construct a general framework of similarity measurement and agglomerative hierarchical clustering algorithm. The algorithm adds information feedback into the agglomerative hierarchical clustering, and specifies similarity measurement according to different stages, so as to make full use of the direction and distance information between pairs of points in clustering. The advantages of the algorithm are as follows: (i) the powerful adaptability allows the algorithm to process complicated data sets without any priors or assumption; (ii) the powerful robustness to noises permits the algorithm to recognize noises without any pretreatment of the data set. The superiority of the algorithm is proved by the application on synthetic and real data sets.