Category: GA
Organization: Graduate School of Business Administration,
        Michigan University, Ann Arbor, MI, USA
Abstract: Information retrieval systems have used clustering
        of documents and queries to improve both retrieval
        efficiency and retrieval effectiveness. Normally,
        clustering involves grouping together static
        descriptions of documents by their similarity to
        each other, though user-based clustering suggests
        that usage patterns concerning co-relevance can form
        a basis for clustering. The author reports that
        clusters of co-relevant documents obtain
        increasingly similar descriptions when a genetic
        algorithm is used to adapt subject descriptions so
        that documents become more effective in matching
        relevant queries and failing to match nonrelevant
        queries. As a result of the increased similarity,
        clustering algorithms can more accurately group
        documents into useful clusters. The findings of this
        work were reached through simulation experiments
Number: 5
Bibtype: Article
Author: M. D. Gordon
Pages: 311-322
Month: jun
Source: Journal of the American Society for Information
        Science
Title: User-based document clustering by redescribing
        subject descriptions with a genetic algorithm
Year: 1991
Volume: 42
Keyword: classification, genetic algorithms, information
        retrieval systems, IRS, information retrieval
        systems, document clustering, subject descriptions,
        genetic algorithm, retrieval efficiency, retrieval
        effectiveness, user-based clustering, usage
        patterns, co-relevance, co-relevant documents,
        simulation experiments