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Titlebook: Data Mining; Special Issue in Ann Robert Stahlbock,Sven F. Crone,Stefan Lessmann Book 2010 Springer-Verlag US 2010 Business Intelligence.Te

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书目名称Data Mining
副标题Special Issue in Ann
编辑Robert Stahlbock,Sven F. Crone,Stefan Lessmann
视频video
概述Provides a state-of-art, up-to-the moment, view of the evolving field of datamining.Special publication of the 2007 International Conference on Data Mining (DMIN 07).Features a combination of rigorous
丛书名称Annals of Information Systems
图书封面Titlebook: Data Mining; Special Issue in Ann Robert Stahlbock,Sven F. Crone,Stefan Lessmann Book 2010 Springer-Verlag US 2010 Business Intelligence.Te
描述.Over the course of the last twenty years, research in data mining has seen a substantial increase in interest, attracting original contributions from various disciplines including computer science, statistics, operations research, and information systems. Data mining supports a wide range of applications, from medical decision making, bioinformatics, web-usage mining, and text and image recognition to prominent business applications in corporate planning, direct marketing, and credit scoring. Research in information systems equally reflects this inter- and multidisciplinary approach, thereby advocating a series of papers at the intersection of data mining and information systems research....This special issue of Annals of Information Systems contains original papers and substantial extensions of selected papers from the 2007 and 2008 International Conference on Data Mining (DMIN’07 and DMIN’08, Las Vegas, NV) that have been rigorously peer-reviewed. The issue brings together topics on both information systems and data mining, and aims to give the reader a current snapshot of the contemporary research and state of the art practice in data mining..
出版日期Book 2010
关键词Business Intelligence; Text Mining; classification; data analysis; data mining; knowledge discovery; stati
版次1
doihttps://doi.org/10.1007/978-1-4419-1280-0
isbn_softcover978-1-4419-1279-4
isbn_ebook978-1-4419-1280-0Series ISSN 1934-3221 Series E-ISSN 1934-3213
issn_series 1934-3221
copyrightSpringer-Verlag US 2010
The information of publication is updating

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Mining Interesting Rules Without Support Requirement: A General Universal Existential Upward Closureient implementations based on the antimonotony property of the support. But candidate set generation is still costly and many rules are uninteresting or redundant. In addition one can miss interesting rules like nuggets. We are thus facing a complexity issue and a quality issue..One solution is to g
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An Extended Study of the Discriminant Random Forested decisions. Many approaches have been developed, but one of the most successful in recent times is the random forest. The discriminant random forest is a novel extension of the random forest classification methodology that leverages linear discriminant analysis to performmultivariate node splittin
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Effects of Oversampling Versus Cost-Sensitive Learning for Bayesian and SVM Classifiersling method called “generative oversampling,” which creates new data points by learning parameters for an assumed probability distribution. We then examine theoretically and empirically the effects of different forms of resampling and their relationship to cost-sensitive learning on different classi
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