Proactive Data Mining with Decision Trees

Nonfiction, Computers, Database Management, Information Storage & Retrievel, General Computing
Cover of the book Proactive Data Mining with Decision Trees by Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon, Springer New York
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Author: Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon ISBN: 9781493905393
Publisher: Springer New York Publication: February 14, 2014
Imprint: Springer Language: English
Author: Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
ISBN: 9781493905393
Publisher: Springer New York
Publication: February 14, 2014
Imprint: Springer
Language: English

This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to suggest specific actions to achieve optimal changes in the value of the target attribute. In particular, the authors suggest a specific implementation of the domain-driven proactive approach for classification trees. The book centers on the core idea of moving observations from one branch of the tree to another. It introduces a novel splitting criterion for decision trees, termed maximal-utility, which maximizes the potential for enhancing profitability in the output tree. Two real-world case studies, one of a leading wireless operator and the other of a major security company, are also included and demonstrate how applying the proactive approach to classification tasks can solve business problems. Proactive Data Mining with Decision Trees is intended for researchers, practitioners and advanced-level students.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to suggest specific actions to achieve optimal changes in the value of the target attribute. In particular, the authors suggest a specific implementation of the domain-driven proactive approach for classification trees. The book centers on the core idea of moving observations from one branch of the tree to another. It introduces a novel splitting criterion for decision trees, termed maximal-utility, which maximizes the potential for enhancing profitability in the output tree. Two real-world case studies, one of a leading wireless operator and the other of a major security company, are also included and demonstrate how applying the proactive approach to classification tasks can solve business problems. Proactive Data Mining with Decision Trees is intended for researchers, practitioners and advanced-level students.

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