Substantial alterations to a system often have little or no effect on particular collections. This may be due to poor separation of relevant and non-relevant documents. The paper presents a procedure for characterizing this separation, which can show whether proposed modifications of the base system are likely to be useful. (8 references)…

Retrieval performance with automatic term classifications for three test collections has been variable. This paper attempts to discover why. The real difference between the collections is in the separation of relevant from non-relevant documents. The separation is so poor that classification cannot be expected to succeed. (14 references)…

The objectives of this paper are to describe the effect of using weighted index terms in a document retrieval system, and to evaluate retrieval performance when queries are expanded by terms occurring in clusters with the query terms. Three data collections, each indexed by several methods, two of which were studied and reported on in previous…

Considers classifications produced by application of single linkage, complete linkage, group average, and word clustering methods to Keen and Cranfield document test collections, and studies structure of hierarchies produced, extent to which methods distort input similarity matrices during classification generation, and retrieval effectiveness…

A measure for the quantification of the changes in classification under small changes in data is proposed. (Author)

Develops a method of rough retrieval, an application of the rough set theory to information retrieval. The aim is to: (1) show that rough sets are naturally applied to information retrieval in which categorized information structure is used; and (2) show that a fuzzy retrieval scheme is induced from the rough retrieval. (AEF)

Discusses object-centered knowledge representation and information retrieval. Highlights include semantic networks; frames; predicative (declarative) and associative knowledge; cluster analysis; creation of subconcepts and superconcepts; automatic classification; hierarchies and pseudohierarchies; graph theory; term classification; clustering of…

Explains a technique whereby a large data base may be automatically classified into maximally connected clusters called cliques. The data base used is a section of United States patents. (Author/ MBR)

Shows how to automatically construct a classification of users and a clustering of documents and cross-references among clusters based on users' information needs. Feedback in the construction of this classification and clustering that allows for the classification to be changed to reflect changing needs of users is also described. (22 references)…

Reviews visualization techniques for scientific disciplines and information retrieval and classification. Highlights include historical background of scientometrics, bibliometrics, and citation analysis; map generation; process flow of visualizing knowledge domains; measures and similarity calculations; vector space model; factor analysis;…

Proposes use of parallel computing systems to overcome the computationally intense clustering process. Results show some near linear speed up in higher threshold clustering applications, meeting the requirements to classify, group and process large document sets within nonprohibitive execution times. Includes graphs and charts. (JAK)

Investigates ways in which people group or categorize documents associated with a voluminous work to guide the construction of organized displays for information retrieval systems. Discusses results of cluster analyses based on "A Christmas Carol" (Charles Dickens) and suggests implications for metadata standards and digital libraries as…

Describes method of automatic document classification in which documents classed as QA by Library of Congress classification system were clustered at six thresholds by keyword using single link technique. Automatically generated clusters were compared to Library of Congress subclasses, and partial classified hierarchy was formed. Twelve references…