Investigates the presence of clustering structure in a document collection and the influence of the presence of clustering structure on the success of cluster-based retrieval. Term-weight and similarity thresholds are discussed, empirical and statistical significance are considered, and indexing exhaustivity for document representation is…

A retrieval system is considered in which document descriptions are stored and accessed in groups called clusters. All items in a cluster meet common similarity criteria and are represented by a composite entity called a profile. In large collections, profiles themselves are clustered and additional levels of profiles are generated. This entire…

This issue contains nine articles that provide an overview of trends and research in parallel information retrieval. Topics discussed include network design for text searching; the Connection Machine System; PThomas, an adaptive information retrieval system on the Connection Machine; algorithms for document clustering; and system architecture for…

Discussion of clustering of documents and queries in information retrieval systems focuses on the use of a genetic algorithm to adapt subject descriptions so that documents become more effective in matching relevant queries. Various types of clustering are explained, and simulation experiments used to test the genetic algorithm are described. (27…

Describes a study conducted on the cystic fibrosis (CF) database, a subset of MEDLINE, that investigated clustering structure and the effectiveness of cluster-based retrieval as a function of the exhaustivity of the uncontrolled subject descriptions. Results are compared to calculations for controlled descriptions based on Medical Subject Headings…