Affinity propagation clustering algorithm based on large-scale data-set

被引：34
|
作者：
Wang L. [1 ]
Zheng K. [1 ]
Tao X. [2 ]
Han X. [3 ]
机构：
[1] School of Management Science and Information Engineering, Jilin University Finance and Economics, Changchun
[2] School of Library Information and Archives Management Engineering, Jilin University, Changchun
[3] School of Computer Science and Engineering, Changchun University of Technology, Jiiln
来源：
International Journal of Computers and Applications | 2018年 / 40卷 / 03期
基金：
中国国家自然科学基金;
关键词：
affinity propagation algorithm; density peak algorithm; Large-scale data-sets; structural similarity;
D O I：
10.1080/1206212X.2018.1425184
中图分类号：
学科分类号：
摘要：
Affinity Propagation (AP) algorithm is not effective in processing large-scale data-sets, so the paper purposed an affinity propagation clustering algorithm based on large scale data-set, called LD-AP. First, we use the idea of grid clustering to divide large data-sets into small datasets and running AP in them to ensure the center of clustering. Then, we introduced the structure similarity matrix to calculate the distance of the cluster center. At last, we used Density peak Clustering Algorithm (DP) algorithm to cluster the cluster again. The experimental results show that the improved algorithm is better than the original algorithm in the clustering effect and computation speed. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
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页码：1 / 6
页数：5
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