Data Mining Techniques for Dynamically Classifying and Analyzing Library Database

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dc.contributor.authorDwivedi, Roopesh Kumaren_US
dc.contributor.authorBajpai, R Pen_US
dc.date.accessioned2007-07-04T07:46:10Zen_US
dc.date.accessioned2010-04-08T09:16:11Z
dc.date.available2007-07-04T07:46:10Zen_US
dc.date.available2010-04-08T09:16:11Z
dc.date.issued2007-02-08en_US
dc.description.abstractHuge amount of data and information is originating in the information era. Library automation can provide some relief, but data mining techniques have to be used for dynamically analyzing the library database and to make strategic decisions for managing the library in an efficient manner. Data mining is the exploration and analysis of large quantities of data in order to discover meaningful patterns and rules. Practical data mining can accomplish a limited set of tasks and only under limited circumstances. For library, it can play an important role by dynamically analyzing library database especially data related to the acquisition and circulation. No single data mining tool and technique is equally applicable. In commercial application, data mining is usually employed on very large database. This paper gives the clear picture of some of the most common association rule data mining techniques which can be applied to the library database and it outcomes.en_US
dc.format.extent150278 bytesen_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.isbn978-81-902079-4-2en_US
dc.identifier.urihttp://hdl.handle.net/1944/572en_US
dc.language.isoenen_US
dc.publisherINFLIBNET Centreen_US
dc.subjectData Warehousingen_US
dc.subjectData Miningen_US
dc.subjectKnowledege Discovery Databaseen_US
dc.subjectNeural Networken_US
dc.subjectDecision Treeen_US
dc.subjectAssociation Ruleen_US
dc.titleData Mining Techniques for Dynamically Classifying and Analyzing Library Databaseen_US
dc.typeArticleen_US

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