원문정보
초록
영어
Traditional affinity propagation algorithm has inefficient results when conducting clustering analysis of high dimensional data because "dimension effect" lead to difficult find the proper class structure .In view of this, the author proposes an improved algorithm on the basis of Entropy Weight Method and Principal Component Analysis (EWPCA-AP). EWPCA-AP algorithm empowers the sample data by Entropy Weight Method, eliminate data irrelevant attributes by Principal Component Analysis, and travel with neighbor clustering algorithm, realization of high-dimensional data clustering in low dimension space. The numerical result of simulation experiment shows that the new EWPCA-AP algorithm can effectively eliminate the redundancy and irrelevant attributes of data and improve the performance of clustering. In addition, the proposed algorithm is applied in the area of the economy in our country and the clustering result is consistent with the real one. This algorithm provides a new intelligent evaluation method for Chinese economy.
목차
1. Introduction
2. Affinity Propagation Clustering Algorithm
3. An Affinity Propagation Algorithm Based on Principal Component Analysis and Entropy Weight Method
3.1 Entropy Weight Method
3.2. Principal Component Analysis
3.3. Affinity Propagation Clustering Algorithm Based on Entropy Weight Methodand Principal Component (EWPCA-AP Clustering Algorithm)
4. Simulation Experiment and Analysis
4.1. Silhouette Effective Index
4.2. Comparison and Analysis
5. Application of EWPCA-AP Clustering Algorithm in China's Regional Economy
5.1. Data Selection
5.2. Clustering Analysis of the Economic Situation of Our China’s Region
6. Conclusions
Acknowledgements
References