| 書目名稱 | Mathematical Tools for Data Mining |
| 副標(biāo)題 | Set Theory, Partial |
| 編輯 | Dan A. Simovici,Chabane Djeraba |
| 視頻video | http://file.papertrans.cn/627/626661/626661.mp4 |
| 概述 | Integrates the mathematics of data mining with its applications.Comprehensive study of set-theoretical and combinatorial foundations of data mining.Provides the necessary mathematical background for r |
| 叢書名稱 | Advanced Information and Knowledge Processing |
| 圖書封面 |  |
| 描述 | This volume was born from the experience of the authors as researchers and educators,whichsuggeststhatmanystudentsofdataminingarehandicapped in their research by the lack of a formal, systematic education in its mat- matics. The data mining literature contains many excellent titles that address the needs of users with a variety of interests ranging from decision making to p- tern investigation in biological data. However, these books do not deal with the mathematical tools that are currently needed by data mining researchers and doctoral students. We felt it timely to produce a book that integrates the mathematics of data mining with its applications. We emphasize that this book is about mathematical tools for data mining and not about data mining itself; despite this, a substantial amount of applications of mathematical c- cepts in data mining are presented. The book is intended as a reference for the working data miner. In our opinion, three areas of mathematics are vital for data mining: set theory,includingpartially orderedsetsandcombinatorics;linear algebra,with its many applications in principal component analysis and neural networks; and probability theory, which plays a fou |
| 出版日期 | Book 20081st edition |
| 關(guān)鍵詞 | Boolean algebra; Clustering; combinatorics; data mining; database; databases; sets |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-84800-201-2 |
| isbn_ebook | 978-1-84800-201-2Series ISSN 1610-3947 Series E-ISSN 2197-8441 |
| issn_series | 1610-3947 |
| copyright | Springer-Verlag London 2008 |
|Archiver|手機版|小黑屋|
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