Earticle

초록

영어

The existing fussy clustering algorithms for uncertain data don’t consider the dynamic cost and the treatment effect is lower, so this paper proposes the dynamic cost-sensitive fussy clustering approach for uncertain data based on the genetic algorithm (GADCSFA). Firstly, this paper gives the definition of dynamic cost and adjacent interval, and the uncertain attributes are disposed as the interval number. Secondly, we give the method of fuzzy c-means clustering based on the interval data, and the interval numbers of fussy clustering solution and cost space are coded by its centre and radius. At last, the dynamic fussy clustering approach for uncertain data based on the genetic algorithm is structured, which uses the genetic algorithm to search the optimal clustering centre and cost by the hybridization, the mutation and selection. The experiments show that, compared to the other fussy clustering algorithm for uncertain data, GADCSFA has higher classification accuracy and performance, and the total expenditure is lower.

목차

Abstract
 1. Introduction
 2. Dynamic Cost
 3. Definition of Adjacent Interval
 4. Fuzzy C-Means Clustering Based on the Interval Data
 5. Dynamic Cost-sensitive Fuzzy Clustering for Uncertain Data Based on the Genetic Algorithm
  5.1. Encoding Scheme
  5.2. Fitness Function
  5.3. Description of Dynamic Cost-sensitive Fuzzy Clustering for Uncertain Data Based on the Genetic Algorithm
 6. Simulation Experiment
 7. Conclusion
 Acknowledgements
 References

키워드

저자

• Yuwen Huang [ Department of Computer and Information Engineering, Heze University, Heze 274015, Shandong, China, Key Laboratory of computer Information Processing, Heze University, Heze 274015, Shandong, 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