Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Partitioning-based clustering for Web document categorization
Decision Support Systems - Special issue on WITS '97
Concept decompositions for large sparse text data using clustering
Machine Learning
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Question classification with support vector machines and error correcting codes
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Nonsmooth Nonnegative Matrix Factorization (nsNMF)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature diversity in cluster ensembles for robust document clustering
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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Unsupervised document classification is an important problem in practical text mining since training data is seldom available. In this paper we study the problem of term selection and the performance of various features for unsupervised text classification. The features studied are: principal components, independent components, and non-negative components. The clustering algorithm used is based on bipartite graph partitioning (Zha et al., 2001). The evaluation is performed using the newsgroups corpus.