Proposes a new approach for classifying text documents into two disjoint classes. Highlights include a brief overview of document clustering; a data mining approach called the One Clause at a Time (OCAT) algorithm which is based on mathematical logic; vector space model (VSM); and comparing the OCAT to the VSM. (Author/LRW)

Proposes and analyzes a method for servicing keyword queries expressed in a digital library. Topics include efficiency via the concept of customers and producers; grouping queries into clusters of similar concepts; information density of the library; query delay; query priorities; and fuzzy decision-making. (Author/LRW)

Presents a document-clustering algorithm that uses a term frequency vector for each document in a Japanese collection to produce a hierarchy in the form of a document classification tree. Introduces an application of this algorithm to a Japanese-to-English translation-aid system. (Author/LRW)

Discusses hierarchic document clustering in information retrieval and its potential improved effectiveness over inverted file search (IFS). Describes experiments conducted to determine whether hierarchic clustering can increase the retrieval effectiveness compared to both static clustering and of conventional IFS when it is applied to search…

Proposes a mining process to automate author co-citation analysis based on the Web Citation Database, a data warehouse for storing citation indices of Web publications. Describes the use of agglomerative hierarchical clustering for author clustering and multidimensional scaling for displaying author cluster maps, and explains PubSearch, a…

Introduces a novel approach to unsupervised text summarization. Proposes an "information-centric" approach to evaluation, where the quality of summaries is judged not in terms of how well they match human-created summaries but in terms of how well they represent their source documents in information retrieval tasks such as document…