An improved affinity propagation clustering algorithm based on principal component analysis and variation coefficient
Limin Wang, Li Zhang, Xuming Han, Na Huang, Xintong Guo · International Journal of Wireless and Mobile Computing · 2014
To solve the 'dimension disaster' problem associated with traditional affinity propagation (AP) algorithm when dealing with high–dimensional data clustering, in this paper we propose an improved AP algorithm named as Affinity Propagation with Coefficient of Variation and Principal Component Analysis (CVPCA–AP). Initially, a dimension reduction was performed on the original data with a focus on keeping most information. Then, the variation coefficient was used to assign a power for the attribute of dimension data. After, the improved AP algorithm was applied for clustering. The simulation results show that the proposed algorithm has superior clustering performance as compared to the traditional AP algorithm. In addition, the proposed algorithm was applied to the clustering analysis for the listed companies, and some satisfactory results were also obtained. This method is enriching studies on intelligence theory and providing a novel reference tool to assist governments in making scientifically sound economic decisions.