Earticle

초록

영어

Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. In this paper, we study on the problem of clustering categorical data, where data objects are made up of non-numerical attributes. We propose MECC (Minimum Error Classification Clustering), an alternative technique for categorical data clustering using VPRS taking into account minimum error classification. The technique is implemented in MATLAB. Experimental results on two benchmark UCI datasets show that MECC technique is better than the baseline categorical data clustering techniques with respect to selecting the clustering attribute.

목차

Abstract
 1. Introduction
 2. Rough Set Theory
  2.1. Information System and Set Approximations
  2.2. Variable Precision Rough Set
 3. Minimum Error Classification Clustering (MECC) Technique
  3.1. The MECC Technique for Selecting Clustering Attribute
  3.2. Example
  3.3. Objects Splitting
 4. Experimental Results
  4.1. Selecting the Clustering Attribute
  4.2. Clustering Objects and Validity
  4.3. Accuracy and Responses Time
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Iwan Tri Riyadi Yanto [ Department of Mathematics University of Ahmad Dahlan ]

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