A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
A course in fuzzy systems and control
A course in fuzzy systems and control
Learning human-like knowledge by singular value decomposition: a progress report
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A fuzzy set-based accuracy assessment of soft classification
Pattern Recognition Letters
Query-sensitive similarity measures for the calculation of interdocument relationships
Proceedings of the tenth international conference on Information and knowledge management
Evaluating strategies for similarity search on the web
Proceedings of the 11th international conference on World Wide Web
Information Retrieval
Evaluating contents-link coupled web page clustering for web search results
Proceedings of the eleventh international conference on Information and knowledge management
A document retrieval system for assisting creative research
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
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Current major search engines on the web retrieve too many documents, of which only a small fraction are relevant to the user query. We propose a new intelligent document filtering algorithm to filter out documents irrelevant to the user query from the output of internet search engines. This algorithm uses output of ‘Google’ search engine as the basic input and processes this input to filter documents most relevant to the query. The clustering algorithm used here is based on the fuzzy c-means with modifications to the membership function formulation and cluster prototype initialisation. It classifies input documents into 3 predefined clusters. Finally, clustered and context-based ranked URLs are presented to the user. The effectiveness of the algorithm has been tested using data provided by the eighth Text REtrieval Conference (TREC-8) [25] and also with on-line data. Experimental results were evaluated by using error matrix method, precision, recall and clustering validity measures.