Algorithms for clustering data
Algorithms for clustering data
Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Automatic text processing
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Braque: design of an interface to support user interaction in information retrieval
Information Processing and Management: an International Journal - Special issue on hypertext and information retrieval
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
SIGDOC '94 Proceedings of the 12th annual international conference on Systems documentation: technical communications at the great divide
Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Almost-constant-time clustering of arbitrary corpus subsets4
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Projections for efficient document clustering
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
Managing Gigabytes: Compressing and Indexing Documents and Images
Managing Gigabytes: Compressing and Indexing Documents and Images
Question-Driven Classification of Retrieved Documents
AUIC '00 Proceedings of the First Australasian User Interface Conference
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In most retrieval systems the answer to a query is a ranked list of documents. There is little information about the ranking and no support for exploring the relationships that may exist between the documents. In this paper we consider the use of clustering answers to better support users satisfying their information needs. We show how clustering reflects the nature of some information needs, and how the clustering can be used to find more relevant documents than would be the case using simple lists. This work contributes to our approach of building answers to information needs, rather than simply providing lists.