원문정보
초록
영어
Given the performance of original affinity propagation algorithm is greatly affected by preference (P), stability threshold-based affinity propagation clustering algorithm (STAP) is proposed in this paper, including stability threshold to obtain the state of convergence when getting real class number and capture the corresponding P, and it take S-type function as damping factor to accelerate the convergence speed of STAP clustering algorithm. Besides it is successfully applied in the financial evaluation of public companies. The simulation experimental results show that, comparing the traditional affinity propagation clustering algorithm, STAP clustering algorithm can obtain high precision and fast convergence rate to improve clustering performance.
목차
1. Introduction
2. Affinity Propagation Clustering Algorithm
3. Stability Threshold-Based Affinity Propagation ClusteringAlgorithm
3.1 The Optimization of Preferences
3.2. The Accelerating Technology with S-type Function
4. Experimental Results and Analysis
5. Experimental Results and Analysis
6. Conclusions
Acknowledgements
References