원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.7 No.6
2014.12
pp.163-174
초록
영어
Rough K-means algorithm has shown that it can provides a reasonable set of lower and upper bounds for a given dataset. With the conceptions of the lower and upper approximate sets, rough k-means clustering and its emerging derivatives become valid algorithms in vague information clustering. However, the most available algorithms ignore the difference of the distances between data objects and cluster centers when computing new mean for each cluster. To solve this issue, an improved algorithm of rough k-means clustering based on variable weighted distance measure is presented in this article. Comparative experimental results of real world data from UCI demonstrate the validity of the proposed algorithm.
목차
Abstract
1. Introduction
2. Related k-means Clustering Algorithms
2.1. Classic Hard k-means Algorithm
2.2. Rough k-means Algorithm
2.3. Improvements of Rough k-means Algorithm
3. Rough k-means Based on Variable Weighted Distance Measure
3.1. Variable Weighted Distance Measure
3.2. Improved Algorithm of Rough k-means Clustering
4. Simulation and Analysis
5. Conclusion
Acknowledgements
References
1. Introduction
2. Related k-means Clustering Algorithms
2.1. Classic Hard k-means Algorithm
2.2. Rough k-means Algorithm
2.3. Improvements of Rough k-means Algorithm
3. Rough k-means Based on Variable Weighted Distance Measure
3.1. Variable Weighted Distance Measure
3.2. Improved Algorithm of Rough k-means Clustering
4. Simulation and Analysis
5. Conclusion
Acknowledgements
References
저자정보
참고문헌
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