Earticle

초록

영어

The Kohonen self organizing map is an excellent tool in exploratory phase of data mining and pattern recognition. The SOM is a popular tool that maps high dimensional space into a small number of dimensions by placing similar elements close together, forming clusters. Recently researchers found that to capture the uncertainty involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcome the uncertainty, a two-level clustering algorithm based on SOM which employs the rough set theory is proposed. The two-level stage Rough SOM (first using SOM to produce the prototypes that are then clustered in the second stage) is found to perform well and more accurate compared with the proposed crisp clustering method (Incremental SOM) and reduces the errors.

목차

Abstract.
 1 Introduction
 2 Self Organizing Map
 3 Rough set Incremental Clustering
  3.1 Incremental Clustering
  3.2 Rough set Incremental Clustering
 4 Rough set Clustering of the Self Organizing Map
 5 Experimentation and Results
 6 Conclusion and Future Work
 References

키워드

저자

• M.N.M. Sap [ Faculty of Computer Science and Information Systems, University Technology Malaysia ]
• Ehsan Mohebi [ Faculty of Computer Science and Information Systems, University Technology Malaysia ]

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