Category cluster discovery from distributed WWW directories
Information Sciences—Informatics and Computer Science: An International Journal - special issue: Knowledge discovery from distributed information sources
Determining the fitness of a document model by using conflict instances
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
A web-page recommender system via a data mining framework and the Semantic Web concept
International Journal of Computer Applications in Technology
Automatic Extraction of Pedagogic Metadata from Learning Content
International Journal of Artificial Intelligence in Education
Identifying document topics using the Wikipedia category network
Web Intelligence and Agent Systems
Estimating the size and evolution of categorised topics in web directories
Web Intelligence and Agent Systems
Document clustering using NMF and fuzzy relation
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Hierarchical role classification based on social behavior analysis
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
Using fuzzy cognitive map to effectively classify e-documents and application
GCC'05 Proceedings of the 4th international conference on Grid and Cooperative Computing
Ontia iJADE: an intelligent ontology-based agent framework for semantic web service
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Granules of words to represent text: an approach based on fuzzy relations and spectral clustering
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part IV
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In this paper, a method of automatically classifying Web documents into a set of categories using the fuzzy association concept is proposed. Using the same word or vocabulary to describe different entities creates ambiguity, especially in the Web environment where theuser population is large. To solve this problem, fuzzy association is used to capture the relationships among different index terms or keywords in the documents, i.e., each pair of words has an associated value to distinguish itself from the others. Therefore, the ambiguity in word usage is avoided. Experiments using data sets collected from two Web portals: Yahoo! (www.yahoo.com) and Open Directory Project (dmoz.org) are conducted. We compare our approach to the vector space model with the cosine coefficient. The results show that our approach yields higher accuracy compared to the vector space model.