Worst-case Analysis of Set Union Algorithms
Journal of the ACM (JACM)
Efficient computation of transitive closures
Fuzzy Sets and Systems
A probabilistic learning approach for document indexing
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
Towards a probabilistic modal logic for semantic-based information retrieval
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
On modeling information retrieval with probabilistic inference
ACM Transactions on Information Systems (TOIS)
A survey of information retrieval and filtering methods
A survey of information retrieval and filtering methods
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A relational data model with fuzzy inheritance dependencies
Fuzzy Sets and Systems
Structured hypertext with domain semantics
ACM Transactions on Information Systems (TOIS)
The fuzzy association degree in semantic data models
Fuzzy Sets and Systems
Machine Learning
Functional dependencies with null values, fuzzy values, and crisp values
IEEE Transactions on Fuzzy Systems
Determining the fitness of a document model by using conflict instances
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
Latent semantic analysis for text categorization using neural network
Knowledge-Based Systems
An automatically constructed thesaurus for neural network based document categorization
Expert Systems with Applications: An International Journal
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This paper discusses the classification problems of text documents. Based on the concept of the proximity degree, the set of words is partitioned into some equivalence classes. Particularly, the concepts of the semantic field and association degree are given in this paper. Based on the above concepts, this paper presents a fuzzy classification approach for document categorization. Furthermore, applying the concept of the entropy of information, the approaches to select key words from the set of words covering the classification of documents and to construct the hierarchical structure of key words are obtained.