Earticle

초록

영어

Fuzzy clustering is a popular unsupervised learning method used in cluster analysis which allows a point in large data sets belongs to two or more clusters. Prior work suggests that Particle Swarm Optimization based approach could be a powerful tool for solving clustering problems. In this paper, we propose a data clustering algorithm based on modified adaptive particle swarm optimization. We choose to use artificial bee colony algorithm combined with PSO technique to modify the traditional clustering methods due to its fast convergence and the presence of adaptive mechanisms based on the evolutionary factor. On the one hand, Particle Swarm Optimization is proven to be an effective and robust technique for fuzzy clustering. On the other hand, the artificial bee colony algorithm has the capability to generate diversity within the swarm when the guide bees are in the exploration mode. Through numerical analysis and experimental simulation, we verify that our algorithm performs much better compared with other state-of-the-art algorithms. Future research schedule is also discussed in the final part.

목차

Abstract
 1. Introduction
 2. Overview of Related Work
 3. Our Proposed Framework for Fuzzy Data Clustering
  3.1. Fuzzy C-means Clustering (FCM)
  3.2. Artificial Bee Colony Algorithm
  3.3. Modified Artificial Bee Colony based Particle Swarm Optimization
  3.4. Detailed Steps of the Proposed ABCPSO Algorithm
 4. Experimental Analysis and Simulation
  4.1. Set-up of the Experiment
  4.2. Experiment and Simulation
 5. Conclusion and Summary
 Acknowledgements
 References

키워드

저자

• Ganglong Duan [ Xi'an University of Technology, Shaanxi 710054, China ]
• Wenxiu Hu [ Xi'an University of Technology, Shaanxi 710054, China ]
• Zhiguang Zhang [ Xi'an University of Technology, Shaanxi 710054, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605