Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Information retrieval
Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
The automatic identification of stop words
Journal of Information Science
Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Detecting content-bearing words by serial clustering—extended abstract
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
An interactive system for finding complementary literatures: a stimulus to scientific discovery
Artificial Intelligence - Special issue on scientific discovery
Clumping properties of content-bearing words
Journal of the American Society for Information Science
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Using latent semantic indexing for literature based discovery
Journal of the American Society for Information Science
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Websom for Textual Data Mining
Artificial Intelligence Review - Special issue on data mining on the Internet
Textual Data Mining to Support Science and Technology Management
Journal of Intelligent Information Systems
Corpus-based statistical screening for content-bearing terms
Journal of the American Society for Information Science and Technology
Citation mining: integrating text mining and bibliometrics for research user profiling
Journal of the American Society for Information Science and Technology
Maximizing Text-Mining Performance
IEEE Intelligent Systems
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Text Mining via Information Extraction
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A Scalable and Efficient Probabilistic Information Retrieval and Text Mining System
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Extraction and representation of contextual information for knowledge discovery in texts
Information Sciences—Informatics and Computer Science: An International Journal
A comparative study for domain ontology guided feature extraction
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
Adapting measures of clumping strength to assess term-term similarity
Journal of the American Society for Information Science and Technology
Letter to the editor: the practice and malpractice of stemming
Journal of the American Society for Information Science and Technology
Text Mining with Information-Theoretic Clustering
Computing in Science and Engineering
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Automatic grammar induction and parsing free text: a transformation-based approach
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Clustering methodologies for identifying country core competencies
Journal of Information Science
Intellectual structure of Korean theology 2000-2008: Presbyterian theological journals
Journal of Information Science
Hi-index | 0.00 |
The presence of trivial words in text databases can affect record or concept (words/phrases) clustering adversely. Additionally, the determination of whether a word/phrase is trivial is context-dependent. Our objective in the present article is to demonstrate a context-dependent trivial word filter to improve clustering quality. Factor analysis was used as a context-dependent trivial word filter for subsequent term clustering. Medline records for Raynaud's Phenomenon were used as the database, and words were extracted from the record abstracts. A factor matrix of these words was generated, and the words that had low factor loadings across all factors were identified, and eliminated. The remaining words, which had high factor loading values for at least one factor and therefore were influential in determining the theme of that factor, were input to the clustering algorithm. Both quantitative and qualitative analyses were used to show that factor matrix filtering leads to higher quality clusters and subsequent taxonomies. © 2005 Wiley Periodicals, Inc.