Please use this identifier to cite or link to this item: http://ir.inflibnet.ac.in/handle/1944/374
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dc.contributor.authorMukhopadhyay, Bikashen_US
dc.contributor.authorMukhopadhyay, Sripatien_US
dc.date.accessioned2004-09-09T10:49:20Zen_US
dc.date.accessioned2010-04-08T08:43:44Z-
dc.date.available2004-09-09T10:49:20Zen_US
dc.date.available2010-04-08T08:43:44Z-
dc.date.issued2004-02en_US
dc.identifier.isbn81-900825-8-2en_US
dc.identifier.urihttp://hdl.handle.net/1944/374en_US
dc.description.abstractData mining automatically and exhaustively explores very large datasets, consequently uncovering otherwise hidden relationships among data. This technology has been successfully applied in science, health, marketing and finance to aid new discoveries and strengthen markets. In addition, data mining techniques are being applied to discover and organize information from the Web. Unfortunately these advancements in data storage and distribution technology have not been accompanied by respective research in data retrieval technology for a long time. To put it in short: we are now being flooded with data, yet we are starving for knowledge. This need has created an entirely new approach to data processing - the data mining, which concentrates on finding important trends and meta-information in huge amounts of raw data. In this paper the main concepts of data mining and automatic knowledge discovery in databases are presented (clustering, finding association rules, categorisation, statistical analysis).en_US
dc.format.extent67237 bytesen_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherINFLIBNET Centre, Ahmedabaden_US
dc.subjectData Miningen_US
dc.subjectText Miningen_US
dc.subjectWeb Searchingen_US
dc.subjectNatural Language Processingen_US
dc.subjectMachine Learningen_US
dc.titleData Mining Techniques for Information Retrievalen_US
dc.typeArticleen_US
Appears in Collections:CALIBER 2004:New Delhi

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