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Titlebook: Data Analytics; Models and Algorithm Thomas A. Runkler Textbook 20203rd edition Springer Fachmedien Wiesbaden GmbH, part of Springer Nature

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書目名稱Data Analytics
副標(biāo)題Models and Algorithm
編輯Thomas A. Runkler
視頻videohttp://file.papertrans.cn/263/262677/262677.mp4
概述A comprehensive introduction.Enabling the reader to design and implement data analytics solutions for real-world applications.Written by a researcher from industry with substantial experience with rea
圖書封面Titlebook: Data Analytics; Models and Algorithm Thomas A. Runkler Textbook 20203rd edition Springer Fachmedien Wiesbaden GmbH, part of Springer Nature
描述This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens.
出版日期Textbook 20203rd edition
關(guān)鍵詞data mining; knowledge discovery; algorithms; forecasting; classification; clustering; business intelligen
版次3
doihttps://doi.org/10.1007/978-3-658-29779-4
isbn_softcover978-3-658-29778-7
isbn_ebook978-3-658-29779-4
copyrightSpringer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020
The information of publication is updating

書目名稱Data Analytics影響因子(影響力)




書目名稱Data Analytics影響因子(影響力)學(xué)科排名




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書目名稱Data Analytics網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Data Analytics被引頻次學(xué)科排名




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Data Preprocessing,bly heterogeneous information sources. We distinguish deterministic and stochastic errors. Deterministic errors can sometimes be easily corrected. Inliers and outliers may be identified and removed or corrected. Inliers, outliers, or noise can be reduced by filtering. We distinguish many different f
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Correlation,ependencies. Nonlinear correlation methods are able to detect nonlinear dependencies but need to be carefully parametrized. As a popular example for nonlinear correlation we present the chi-square test for independence that can be applied to continuous features using histogram counts. Nonlinear corr
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Regression,d to linear dependencies. Substitution allows to identify specific types of nonlinear dependencies by linear regression. Robust regression finds models that are robust against inliers or outliers. A popular class of nonlinear regression methods are universal approximators. We present two well-known
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