Earticle

초록

영어

In pattern recognition, clustering is a powerful technique that can be used to find the identical group of objects from a given dataset. It has proven its importance in various domains such as bioinformatics, machine learning, pattern recognition, document clustering and so on. But, in clustering, it is difficult to determine the optimal cluster centers in a given set of data. So, in this paper, a new method called magnetic charged system search (MCSS) is applied to determine the optimal cluster centers. This method based on the behavior of charged particles. The proposed method employs the electric force and magnetic force to initiate the local search while Newton second law of motion is employed for global search. The performance of the proposed algorithm is tested on several datasets which are taken from UCI repository and compared with the other existing methods like K-Means, GA, PSO, ACO and CSS. The experimental results prove the applicability of the proposed method in clustering domain.

목차

Abstract
 1. Introduction
 2. Clustering Problem
 3. Magnetic Charge System Search (MCSS) Algorithm
  3.1 Pseudo code of MCSS algorithm for clustering
 4. Experimental Results
  4.1. Performance Measures
  4.2 Parameters Settings
  4.3. Datasets
  4.4. Experiment Results
  4.5. Statistical Analysis
 5. Conclusion
 References

키워드

저자

• Yugal Kumar [ Department of Information Technology, Krishna Institute of Engineering and Technology, Ghaziabad, India ]
• Sumit Gupta [ Department of Information Technology, Krishna Institute of Engineering and Technology, Ghaziabad, India ]
• G. Sahoo [ Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605