Earticle

초록

영어

Heterogeneous data stream clustering is an important issue in data stream mining, for the accuracy of the existing heterogeneous clustering algorithm is not high, and don’t have a common distance measure method, a heterogeneous data stream clustering algorithm based on the density with mixed distance measure method is proposed. HDSDen algorithm adopts an online/offline two-stage processing framework. According to the situation of dominant property, the online stage use corresponding distance measure method to define the core points among the arriving points, the purpose of the different distance calculation method is to reduce the influence of the non-dominant property on the whole clustering accuracy. All the density-reachable points form a cluster in the offline stage, and put all the not-clustered points into the reservoir, and the number of the reservoir exceeds the threshold value, we will re-cluster the points to improve the accuracy of clustering. Experiments on real data sets show that the algorithm can achieve better clustering results, and give the clustering results at any time, which can deal with the heterogeneous data stream efficiently.

목차

Abstract
 1. Introduction
 2. The Traditional Density-based Clustering and Related Definitions
 4. HDSDen Algorithm Framework and Related Concepts
  4.1. Algorithm Framework
  4.2. Distance Measure Method
 5. Detailed Description of HDSDen Algorithm
  5.1. Pretreatment Process
  5.2. Online Maintenance
  5.3. Offline Stage
  5.4 The Process of HDSDen Algorithm
 6. Experimental Evaluation
  6.1. Categorical Attributes Dominant Data
  6.2. Continuous Attributes Dominant Data
 7. Conclusion
 References

키워드

저자

• Chen Jin-yin [ Zhejiang University of Technology, Hangzhou310000 ]
• He Hui-hao [ Zhejiang University of Technology, Hangzhou310000 ]

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