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dc.contributor.authorVoorhees, Ellen M.en_US
dc.date.accessioned2007-04-23T17:15:11Z
dc.date.available2007-04-23T17:15:11Z
dc.date.issued1986-07en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR86-765en_US
dc.identifier.urihttps://hdl.handle.net/1813/6605
dc.description.abstractSearching hierarchically clustered document collections can be effective, but creating the cluster hierarchies is expensive since there are both many documents and many terms. However, the information in the document-term matrix is sparse: documents are usually indexed by relatively few terms. This paper describes the implementations of three agglomerative hierarchic clustering algorithms that exploit this sparsity so that collections much larger than the algorithms' worst case running times would suggest can be clustered. The implementations described in the paper have been used to cluster a collection of 12,000 documents.en_US
dc.format.extent1615880 bytes
dc.format.extent457001 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleImplementing Agglomerative Hierarchic Clustering Algorithms for Use in Document Retrievalen_US
dc.typetechnical reporten_US


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