Semi-supervised Affinity Propagation Clustering Based on Subtractive Clustering for Large-Scale Data Sets

作     者：Qi Zhu Huifu Zhang Quanqin Yang 

作者机构：School of Computer Science and EngineeringHunan University of Science and TechnologyXiangtan411201China 

出 版 物：《国际计算机前沿大会会议论文集》 (International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）)

年 卷 期：2015年第1期

页      面：76-77页

学科分类：0303[法学-社会学] 03[法学] 

基　　金：This research has been partially supported by the national natural science foundation of China (51175169) and the national science and technology support program (2012BAF02B01) 

主　　题：subtractive clustering initial cluster affinity propagation clustering semi-supervised clustering large-scale data sets 

摘      要：In the face of a growing number of large-scale data sets, affinity propagation clustering algorithm to calculate the process required to build the similarity matrix, will bring huge storage and computation. Therefore, this paper proposes an improved affinity propagation clustering algorithm. First, add the subtraction clustering, using the density value of the data points to obtain the point of initial clusters. Then, calculate the similarity distance between the initial cluster points, and reference the idea of semi-supervised clustering, adding pairs restriction information, structure sparse similarity matrix. Finally, the cluster representative points conduct AP clustering until a suitable cluster *** results show that the algorithm allows the calculation is greatly reduced, the similarity matrix storage capacity is also reduced, and better than the original algorithm on the clustering effect and processing speed.