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dc.contributor.authorKanimozhi, K.V.
dc.contributor.authorPrabhavathy, P.
dc.contributor.authorVenkatesan, M.
dc.date.accessioned2020-03-30T09:46:06Z-
dc.date.available2020-03-30T09:46:06Z-
dc.date.issued2018
dc.identifier.citationAdvances in Intelligent Systems and Computing, 2018, Vol.706, , pp.585-594en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/6766-
dc.description.abstractDue to the advance Internet and increasing globalization, the electronics forms of information grow in a rapid manner. Extracting the useful hidden information from those multiple documents is a recent challenge. Hence, efficient and automated clustering algorithm which is effective in identifying topics plays the main role in information retrieval. In this paper, the analysis regarding the large unstructured text document corpus using our proposed map-reduce algorithm has been performed, and the results show the advantage of the proposed method by detecting clusters of document features within less computation time and provides premier solution for increasing the precision rate of retrieval in information extraction. � 2018, Springer Nature Singapore Pte Ltd.en_US
dc.titleText document analysis using map-reduce frameworken_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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