Earticle

초록

영어

We extended the online learning strategy and scalable clustering technique to soft subspace clustering, and propose two online soft subspace clustering methods, OFWSC and OEWSC. The proposed evolving soft subspace clustering algorithms can not only reveal the important local subspace characteristics of high dimensional data, but also leverage on the effectiveness of online learning scheme, as well as the ability of scalable clustering methods for the large or streaming data. Furthermore, we apply our proposed algorithms to text clustering of information retrieval, gene expression data clustering, face image classification and the problem of predicting disulfide connectivity.

목차

Abstract
 1. Introduction
 2. Online Soft Subspace Clustering Algorithm
  2.1 Online Learning Strategy based on Competitive Learning Theory
  2.2 Online Fuzzy Weighted Soft Subspace Clustering
  2.2 Online Entropy Weighted Soft Subspace Clustering
 3 Experiment Design and Discussion
  3.1. Parameter Setting and Experimental Arrangement
  3.2. Evaluation Criteria
  3.3. Comparison of Online Soft Subspace Clustering Algorithms
 4 Conclusion
 References

키워드

저자

• Runtao Lv [ Baotou light industry professional technology institute college of electronic commerce, Baotou, china ]
• Jin Kao Zhao [ Baotou light industry professional technology institute college of electronic commerce, Baotou, china ]
• Yu Li [ Baotou city bureau of education test center, Baotou, 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