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
SONIA: a service for organizing networked information autonomously
Proceedings of the third ACM conference on Digital libraries
A semi-supervised document clustering technique for information organization
Proceedings of the ninth international conference on Information and knowledge management
Machine Learning
Athena: Mining-Based Interactive Management of Text Database
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Integrating Declarative Knowledge in Hierarchical Clustering Tasks
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Using unlabeled data to improve text classification
Using unlabeled data to improve text classification
Techniques for improving the performance of naive bayes for text classification
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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This paper presents a series of text-mining algorithms for managing knowledge directory, which is one of the most crucial problems in constructing knowledge management systems today. In future systems, the constructed directory, in which knowledge objects are automatically classified, should evolve so as to provide a good indexing service, as the knowledge collection grows or its usage changes. One challenging issue is how to combine manual and automatic organization facilities that enable a user to flexibly organize obtained knowledge by the hierarchical structure over time. To this end, I propose three algorithms that utilize text mining technologies: semi-supervised classification, semi-supervised clustering, and automatic directory building. Through experiments using controlled document collections, the proposed approach is shown to significantly support hierarchical organization of large electronic knowledge base with minimal human effort.