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Data Mining in Finance
Kovalerchuk, Boris; Vityaev, Evgenii
€ 249.76
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Description for Data Mining in Finance
Paperback. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 308 pages, biography. BIC Classification: KFF; UMB; UYQ. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 18. Weight in Grams: 504.
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 better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain ... Read more
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 better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain ... Read more
Product Details
Format
Paperback
Publication date
2013
Publisher
Springer-Verlag New York Inc. United States
Number of pages
308
Condition
New
Series
The Springer International Series in Engineering and Computer Science
Number of Pages
308
Place of Publication
New York, NY, United States
ISBN
9781475773323
SKU
V9781475773323
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
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