Earticle

초록

영어

Clustering is one of the main tasks used in pattern recognition and classification. Out of many methods that have been reported till date the most widely used methods are based on likelihood approach of mixture model. Among different mixture models, Expectation Maximization for Gaussian Mixture is most exploited and trusted algorithm for data clustering. However, it has some short comings such as initial parameters are to be given a-priori, convergence speed is slow and the results obtained are highly dependent upon the initial parameters. Many variations have been carried out in implementing EM algorithm but still there is ample scope for improvement. The proposed algorithm tries to overcome these shortcomings and provide more robust and efficient version of clustering algorithm. An improvement related to cluster partitioning is proposed in the existing algorithm resulting some advantages. The robustness and efficacy of the algorithm is demonstrated qualitatively as well as quantitatively with the help of some experiments.

목차

Abstract
 1. Introduction
 2. Review of Background
  2.1. Overview of existing EM algorithm for Gaussian Mixture Model [13]
 3. Proposed Method
 4. Results and Discussions
  4.1. Experiment 1
  4.2. Experiment 2
  4.3. Experiment 3
 5. Conclusion
 References

키워드

저자

• Aakash Soor [ Department of Electronics & Communication Engineering National Institute of Technology Kurukshetra, Haryana, India ]
• Vikas Mittal [ Department of Electronics & Communication Engineering National Institute of Technology Kurukshetra, Haryana, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605