Search result clustering through expectation maximization based pruning of terms

dc.contributor.author	Hima Bindu, K.
dc.contributor.author	Raghavendra Rao, C.
dc.date.accessioned	2022-03-27T05:59:13Z
dc.date.available	2022-03-27T05:59:13Z
dc.date.issued	2014-01-01
dc.description.abstract	Search Results Clustering (SRC) is a well-known approach to address the lexical ambiguity issue that all search engines suffer from. This paper develops an Expectation Maximization (EM)-based adaptive term pruning method for enhancing search result analysis. Knowledge preserving capabilities of this approach are demonstrated on the AMBIENT dataset using Snowball clustering method.
dc.identifier.citation	Advances in Intelligent Systems and Computing. v.236
dc.identifier.issn	21945357
dc.identifier.uri	10.1007/978-81-322-1602-5_134
dc.identifier.uri	http://link.springer.com/10.1007/978-81-322-1602-5_134
dc.identifier.uri	https://dspace.uohyd.ac.in/handle/1/9018
dc.subject	Clustering
dc.subject	Expectation maximization
dc.subject	FPtree
dc.subject	Information retrieval
dc.title	Search result clustering through expectation maximization based pruning of terms
dc.type	Book Series. Conference Paper
dspace.entity.type

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