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Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence)
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Lin, T. y.; Lin, Tsau Young; Ohsuga, Setsuo 作者
Springer
譯者
2005-12 出版日期
392 頁數
USD 169.00 價格
Hardcover
叢書系列
9783540283157 圖書編碼

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發表於2024-11-27

Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence) 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence) 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


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Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence) 在線電子書 著者簡介

Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence) 在線電子書 著者簡介

Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence) 在線電子書 圖書描述

Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving. Researchers, on the other hand, lack an understanding of the practical issues of data-mining for real-world problems and often concentrate on issues that are of no significance to the practitioners. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining. We have also called on researchers with practical data mining experiences to present new important data-mining topics.

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