Feature Reduction for Document Clustering and Classification

Feature Reduction for Document Clustering and Classification (2000)

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by S M Rüger , S E Gauch

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Datum	Value	Source
TITLE	Feature Reduction for Document Clustering and Classification	SVM HeaderParse 0.2
AUTHOR NAME	S M Rüger	SVM HeaderParse 0.2
AUTHOR AFFIL	1 Department of Computing; Imperial College of Science, Technology and Medicine	SVM HeaderParse 0.2
AUTHOR ADDR	180 Queen’s Gate, London SW7 2BZ, England	SVM HeaderParse 0.2
AUTHOR NAME	S E Gauch	SVM HeaderParse 0.2
AUTHOR AFFIL	1 Department of Computing; Imperial College of Science, Technology and Medicine	SVM HeaderParse 0.2
AUTHOR ADDR	180 Queen’s Gate, London SW7 2BZ, England	SVM HeaderParse 0.2
ABSTRACT	Often users receive search results which contain a wide range of documents, only some of which are relevant to their information needs. To address this problem, ever more systems not only locate information for users, but also organise that information on their behalf. We look at two main automatic approaches to information organisation: interactive clustering of search results and pre-categorising documents to provide hierarchical browsing structures. To be feasible in real world applications, both of these approaches require accurate yet efficient algorithms. Yet, both suffer from the curse of dimensionality — documents are typically represented by hundreds or thousands of words (features) which must be analysed and processed during clustering or classification. In this paper, we discuss feature reduction techniques and their application to document clustering and classification, showing that feature reduction improves efficiency as well as accuracy. We validate these algorithms using human relevance assignments and categorisation. 1	SVM HeaderParse 0.2
YEAR	2000	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Technical report	INFERENCE
CITATIONS	27 found	ParsCit 1.0