원문정보
보안공학연구지원센터(IJSEIA)
International Journal of Software Engineering and Its Applications
Vol.7 No.5
2013.09
pp.221-232
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
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