| 期刊全稱(chēng) | Advanced Data Mining Techniques | | 影響因子2023 | David L. Olson,Dursun Delen | | 視頻video | http://file.papertrans.cn/146/145472/145472.mp4 | | 發(fā)行地址 | Includes supplementary material: | | 圖書(shū)封面 |  | | 影響因子 | The intent of this book is to describe some recent data mining tools that have proven effective in dealing with data sets which often involve unc- tain description or other complexities that cause difficulty for the conv- tional approaches of logistic regression, neural network models, and de- sion trees. Among these traditional algorithms, neural network models often have a relative advantage when data is complex. We will discuss methods with simple examples, review applications, and evaluate relative advantages of several contemporary methods. Book Concept Our intent is to cover the fundamental concepts of data mining, to dem- strate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. We have organized the material into three parts. Part I introduces concepts. Part II contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining. Not all of these chapters need to be covered, and their sequence could be varied at instructor design. The book will include short vignettes of how specific concepts have been applied in real practice. A series of re | | Pindex | Book 2008 |
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