[PDF.64qv] Data Mining in Finance: Advances in Relational and Hybrid Methods (The Springer International Series in Engineering and Computer Science)

Data Mining in Finance: Advances in Relational and Hybrid Methods (The Springer International Series in Engineering and Computer Science)

[PDF.xc55] Data Mining in Finance: Advances in Relational and Hybrid Methods (The Springer International Series in Engineering and Computer Science)

Data Mining in Finance:  Boris Kovalerchuk, Evgenii Vityaev epub
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 | #4794935 in Books |  2000-03-01 | Original language:English | PDF # 1 |  9.21 x.81 x6.14l,1.31 | File type: PDF | 308 pages

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||0 of 0 people found the following review helpful.| Good examples but missing explanations|By Sandro Saitta|With a title such as "Data Mining in Finance: Advances in Relational and Hybrid Methods", one can think that this book is a set of research papers in this topic. However, it is not the case. The authors, Kovalerchuk and Vityaev, have written more than 300 pages about applying data mining techniques in finance.

A

Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus ...

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