The UNIX command reference guide
The UNIX command reference guide
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
A self-organizing semantic map for information retrieval
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
An Information Retrieval Approach for Automatically Constructing Software Libraries
IEEE Transactions on Software Engineering
Introduction to information storage and retrieval systems
Information retrieval
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
A graphical, self-organizing approach to classifying electronic meeting output
Journal of the American Society for Information Science
Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Self-organizing maps
Feature selection and effective classifiers
Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
Applied Intelligence
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
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The self-organizing map (SOM) can classify documents by learning about their interrelationships from its input data. The dimensionality of the SOM input data space based on a document collection is generally high. As the computational complexity of the SOM increases in proportion to the dimension of its input space, high dimensionality not only lowers the efficiency of the initial learning process but also lowers the efficiencies of the subsequent retrieval and the relearning process whenever the input data is updated. A new method called ifeature competitive algorithm (FCA) is proposed to overcome this problem. The FCA can capture the most significant features that characterize the underlying interrelationships of the entities in the input space to form a dimensionally reduced input space without excessively losing of essential information about the interrelationships. The proposed method was applied to a document collection, consisting of 97 UNIX command manual pages, to test its feasibility and effectiveness. The test results are encouraging. Further discussions on several crucial issues about the FCA are also presented.