Sign In | Not yet a member? | Submit your article
 
Home   Technical   Study   Novel   Nonfiction   Health   Tutorial   Entertainment   Business   Magazine   Arts & Design   Audiobooks & Video Training   Cultures & Languages   Family & Home   Law & Politics   Lyrics & Music   Software Related   eBook Torrents   Uncategorized  
Letters: A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Data Mining in Finance: Advances in Relational and Hybrid Methods
Data Mining in Finance: Advances in Relational and Hybrid Methods
Date: 28 April 2011, 12:39

Free Download Now     Free register and download UseNet downloader, then you can FREE Download from UseNet.

    Download without Limit " Data Mining in Finance: Advances in Relational and Hybrid Methods " from UseNet for FREE!
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 the discovered rules -- an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.

DISCLAIMER:

This site does not store Data Mining in Finance: Advances in Relational and Hybrid Methods on its server. We only index and link to Data Mining in Finance: Advances in Relational and Hybrid Methods provided by other sites. Please contact the content providers to delete Data Mining in Finance: Advances in Relational and Hybrid Methods if any and email us, we'll remove relevant links or contents immediately.


Posts related to "Data Mining in Finance: Advances in Relational and Hybrid Methods"
Multi-Relational Data Mining Multi-Relational Data Mining

ISBN/ASIN: 1586036610
Total size: N/A
Managing and Mining Graph Data (Advances in Database Systems) Managing and Mining Graph Data (Advances in Database Systems)

ISBN/ASIN: 1441960449
Total size: 102 MB
Mining Massive Data Sets for Security: Advances in Data Mining, Search, Social Networks and Text Mining Mining Massive Data Sets for Security: Advances in Data Mining, Search, Social Networks and Text Mining

ISBN/ASIN: 1586038982
Total size: 7.2 MB
Recent Advances In Data Mining Of Enterprise Data: Algorithms and Applications Recent Advances In Data Mining Of Enterprise Data: Algorithms and Applications

ISBN/ASIN: 981277985X
Total size: 8 MB
Advances in Knowledge Discovery and Data Mining, Part II Advances in Knowledge Discovery and Data Mining, Part II

ISBN/ASIN: 3642136710
Total size: 8.7 MB
Advances in Knowledge Discovery and Data Mining, Part I Advances in Knowledge Discovery and Data Mining, Part I

ISBN/ASIN: 3642136567
Total size: 7.9 MB
Advances in Data Analysis: Theory and Applications to Reliability and Inference, Data Mining, Bioinformatics, Lifetime Data, and Neural Networks Advances in Data Analysis: Theory and Applications to Reliability and Inference, Data Mining, Bioinformatics, Lifetime Data, and Neural Networks

ISBN/ASIN: 0817647988
Total size: 3 MB
Data Warehousing and Data Mining Techniques for Cyber Security (Advances in Information Security) Data Warehousing and Data Mining Techniques for Cyber Security (Advances in Information Security)

ISBN/ASIN: 0387264094
Total size: 6.86 MB
Perception-based Data Mining and Decision Making in Economics and Finance Perception-based Data Mining and Decision Making in Economics and Finance

ISBN/ASIN: 3540362444
Total size: 4 MB

Comments

Comments (0) All

Verify: Verify

    Sign In   Not yet a member?


Popular searches
running