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Titlebook: Soft Computing for Data Mining Applications; K. R. Venugopal,K. G. Srinivasa,L. M. Patnaik Book 2009 Springer-Verlag Berlin Heidelberg 200

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書目名稱Soft Computing for Data Mining Applications
編輯K. R. Venugopal,K. G. Srinivasa,L. M. Patnaik
視頻videohttp://file.papertrans.cn/871/870469/870469.mp4
概述Recent research in the fields of Data Mining in combination with Soft Computing methodologies.State-of-the-art technology in data mining.Includes supplementary material:
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Soft Computing for Data Mining Applications;  K. R. Venugopal,K. G. Srinivasa,L. M. Patnaik Book 2009 Springer-Verlag Berlin Heidelberg 200
描述The authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications. The monograph gives an insight into the research in the ?elds of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow - ponentially. Much of the data is implicitly or explicitly imprecise. Database discovery seeks to discover noteworthy, unrecognized associations between the data items in the existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. The rate at which the data is storedis growing at a phenomenal rate. Asaresult,traditionaladhocmixturesofstatisticaltechniquesanddata managementtools are no longer adequate for analyzing this vast collection of data. Severaldomainswherelargevolumesofdataarestoredincentralizedor distributeddatabasesincludesapplicationslikeinelectroniccommerce,bio- formatics, computer security, Web intelligence, intelligent learning database systems,?nance,marketing,healthcare,telecommunications,andother?elds. E?cient tools and algorithms for knowledge discovery in large data sets have been devised during the
出版日期Book 2009
關(guān)鍵詞Racter; Web Intelligence; algorithms; bioinformatics; data mining; evolution; genetic algorithm; genome; lea
版次1
doihttps://doi.org/10.1007/978-3-642-00193-2
isbn_softcover978-3-642-10125-0
isbn_ebook978-3-642-00193-2Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2009
The information of publication is updating

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Studies in Computational Intelligencehttp://image.papertrans.cn/s/image/870469.jpg
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https://doi.org/10.1007/978-3-642-00193-2Racter; Web Intelligence; algorithms; bioinformatics; data mining; evolution; genetic algorithm; genome; lea
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Dynamic Association Rule Mining Using Genetic Algorithms,, with Fast UPdate (FUP) algorithm for intra transactions and E-Apriori for inter transactions. Our study shows that the algorithm DMARG outperforms both FUP and E-Apriori in terms of execution time and scalability, without compromising the quality or completeness of rules generated.
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