Earticle

초록

영어

For the fact that telecom data size is extremely huge and the management is much complicated, the paper proposes subspace clustering algorithm based on multi-rule constraint, to mine business knowledge information in a more efficient and accurate manner. By relying on K-means clustering algorithm, the method improves selection and mutation operation of genetic algorithms and thus corrects inappropriate choice of K-means initial clustering centers. Meanwhile, with the use of variable weighting strategy, data classification sparseness in the clustering is overcome. A fast and useful mining method is enabled for massive data. Results show its better performance in terms of computing efficiency, accuracy and ability.

목차

Abstract
 1. Introduction
 2. Application and Analysis of the Method
 3. Experiment Design and Discussion
  3.1. Test Scenario and Data
  3.2. Analysis of Telecom Business Application
  3.3. Data Magnitude Test and Analysis
  3.4. Performance Validation and Analysis
  3.5. Emulation Test
 4. Conclusion
 References

키워드

저자

• Huiping Li [ Bao Tou Medical College Inner Mongolia Bao Tou 014040, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605