A nonnegative matrix factorization framework for semi-supervised document clustering with dual constraints

Huifang Ma; Weizhong Zhao; Zhongzhi Shi

doi:10.1007/s10115-012-0560-3

ScienceGate Book Chapters

JOURNAL ARTICLE

A nonnegative matrix factorization framework for semi-supervised document clustering with dual constraints

Huifang Ma Weizhong Zhao Zhongzhi Shi

Year: 2012 Journal: Knowledge and Information Systems Vol: 36 (3)Pages: 629-651 Publisher: Springer Science+Business Media

DOI: 10.1007/s10115-012-0560-3

Get Full-Text PDF Get Analytical Report

Keywords:

Cluster analysis Non-negative matrix factorization Correctness Document clustering Constrained clustering Correlation clustering Canopy clustering algorithm Data stream clustering Computer science CURE data clustering algorithm Biclustering Fuzzy clustering Mathematics Data mining Artificial intelligence Matrix decomposition Algorithm

Metrics

Cited By

1.89

FWCI (Field Weighted Citation Impact)

Refs

0.88

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Text and Document Classification Technologies

Physical Sciences → Computer Science → Artificial Intelligence

Advanced Clustering Algorithms Research

Physical Sciences → Computer Science → Artificial Intelligence

Data Management and Algorithms

Physical Sciences → Computer Science → Signal Processing

A nonnegative matrix factorization framework for semi-supervised document clustering with dual constraints

Metrics

Citation History

Topics

Related Documents

Semi-supervised nonnegative matrix factorization with pairwise constraints for image clustering

Orthogonal Nonnegative Matrix Tri-factorization for Semi-supervised Document Co-clustering

Semi-supervised Nonnegative Matrix Factorization for Document Classification

Self-supervised semi-supervised nonnegative matrix factorization for data clustering

Dual constraint based semi-supervised nonnegative matrix factorization for multi-view clustering