Earticle

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

키워드

저자

• Tengfei Zhang [ College of Automation, Nanjing University of Posts and Telecommunications, Nanjing China ] Corresponding Author
• Long Chen [ College of Automation, Nanjing University of Posts and Telecommunications, Nanjing China ]
• Fumin Ma [ College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, China ]

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