원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.9 No.12
2016.12
pp.217-230
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보