Earticle

초록

영어

Subspace outlier mining has a very important significance in big data analysis. To a large extent, subspace clustering algorithm has impact on the efficiency of mining outliers in subspaces. To solve the problem that CMI method selects best clustering subspaces unstably and complexly, formulas of chain rule of Cumulative Entropy, Cumulative Total Correlation and Cumulative Holoentropy were given. Cumulative Holoentropy was used to mine the best clustering subspaces on continuous data sets in which outliers were detected. Subspace outlier detection algorithm based on Cumulative Holoentropy was then proposed. Finally, the validity and scalability of proposed method were tested on real datasets and virtual datasets. Experiment shows that the efficiency of mining outliers in subspaces is enhanced by the proposed algorithm.

목차

Abstract
 1. Introduction
 2. Basic Definitions
 3. Holoentropy Measure Subspace Clustering
 4. SODCH algorithm
  4.1 Descripted of SODCH Algorithm
  4.2 Process of SODCH Algorithm
 5. The Experimental Results and Analysis
  5.1 Real Data Sets
  5.2 Virtual Data Sets
 ACKNOWLEDGEMENTS
 References

키워드

저자

• Zhang Zhong-ping [ School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China, The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, Hebei 066004, China ]
• Sun Ying [ School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China ]
• Fang Chun-zhen [ School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China ]
• Wang Ying [ School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605