A fuzzy document retrieval system using the keyword connection matrix and a learning method
Fuzzy Sets and Systems - Special issue on applications of fuzzy systems theory, Iizuka '88
Elements of information theory
Elements of information theory
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Proceedings of the ninth international conference on Information and knowledge management
Machine Learning
Information Retrieval: Algorithms and Heuristics
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Combining preference- and content-based approaches for improving document clustering effectiveness
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Decision Support Systems
A Latent Semantic Indexing-based approach to multilingual document clustering
Decision Support Systems
User Oriented Hierarchical Information Organization and Retrieval
ECML '07 Proceedings of the 18th European conference on Machine Learning
Managing Word Mismatch Problems in Information Retrieval: A Topic-Based Query Expansion Approach
Journal of Management Information Systems
Combining preference- and content-based approaches for improving document clustering effectiveness
Information Processing and Management: an International Journal
Text mining documents in electronic data interchange environment
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Using text mining techniques in electronic data interchange environment
WSEAS Transactions on Computers
Machine Learning
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Document clustering is inherently an unsupervised learning process that organizes document (or text) data into distinct groups without depending on pre-specified knowledge. However, real-world applications, such as building a topical hierarchy for a large document collection, need to perform clustering under various kinds of constraints. This paper presents a new type of supervised clustering to organize information in a way that reflects knowledge provided by a user. As a means by which external human knowledge can be incorporated into the clustering process, a quadratic form distance metric is employed that contains a weight matrix. Also, we propose a way of representing knowledge to guide the clustering process and a variant of the gradient descent search technique to find a user-specific weight matrix under the hierarchical clustering strategy.