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On-line legal aid: Markov chain model for efficient retrieval of legal documents

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4 Author(s)
Ghosh-Roy, R. ; Dept. of Electr. Eng. & Electron., Brunel Univ., Uxbridge, UK ; Habiballah, I.O. ; Stonham, T.J. ; Irving, M.R.

It is widely accepted that, with large databases, the key to good performance is effective data-clustering. In any large document database clustering is essential for efficient search, browse and therefore retrieval. Cluster analysis allows the identification of groups, or clusters, of similar objects in multi-dimensional space. Conventional document retrieval systems involve the matching of a query against individual documents, whereas a clustered search compares a query with clusters of documents, thereby achieving efficient retrieval. In most document databases periodic updating of clusters is required due to the dynamic nature of a database. Experimental evidence, however shows that clustered searches are substantially less effective than conventional searches of corresponding non-clustered documents. We investigate the present clustering criteria and its drawbacks. We propose a new approach to clustering and justify the reasons why this new approach should be tested and (if proved beneficial) adopted

Published in:

Document Image Processing and Multimedia Environments, IEE Colloquium on

Date of Conference:

2 Nov 1995

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