Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Web Page Clustering Using a Fuzzy Logic Based Representation and Self-Organizing Maps
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Information extraction from syllabi for academic e-Advising
Expert Systems with Applications: An International Journal
Supervised and Traditional Term Weighting Methods for Automatic Text Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The present work briefly describes a novel approach for categorizing semi-structure documents by using fuzzy rule-based system. We propose fuzzy logic representation for semi-structured documents and then by proposing new metric, categorize documents into different classes. The idea behind of our approach is to divide web pages into different semantic sections and by using fuzzy logic system extract features and weight harvested terms to represent semi-structure documents. A set of metrics are also used to measure similarity between documents based on the weight of each region in the text. A clustering algorithm is also explained that categorized documents into several categories. This idea is inspired as a subfield of the area of Matchmaking that tries to match document creators and users in order to find the best similarities between them and connect them for further collaborations.