Earticle

초록

영어

Affinity propagation algorithm is a new powerful and effective clustering method. One of the major problems in clustering is the determination of the optimal number of clusters. In this paper, the particle swarm optimization algorithm is utilized to cope with this problem by using the parameter p as each particle and Silhouette index as the fitness, which can search for the optimal value of p and determine the optimal number of clusters automatically. Moreover, the problem of information overlap is the main drawback of affinity propagation algorithm in dealing with complex structure or high dimensional data for clustering. Hence the enhanced Locality preserving projections method is proposed to integrate with affinity propagation algorithm to reduce the dimension of the data as a processing step. As the result of experiment shows, the proposed method can simultaneously obtain the optimal number of clusters accurately and improve the clustering accuracy by eliminating the redundant information of the data without losing the internal nonlinear structure.

목차

Abstract
 1. Introduction
 2. Preliminary
  2.1. Affinity Propagation Algorithm
  2.2. Particle Swarm Optimization Algorithm
 3. Proposed Method
  3.1. Obtain the Optimal Parameter By PSO
  3.2. The enhanced Locality Preserving Projections
 4. Experiment and Analysis
 5. Conclusion
 Acknowledgements
 Reference

키워드

저자

• Lele Liu [ Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi, 214122, China. ]
• Zhiping Zhou [ Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi, 214122, China. ]
• Jiefeng Wang [ Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi, 214122, China. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605