Proactive Data Mining with Decision Trees

✍ Scribed by Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon (auth.)

• Publisher: Springer-Verlag New York
• Year: 2014
• Tongue: English
• Leaves: 94
• Series: SpringerBriefs in Electrical and Computer Engineering
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-x
Introduction to Proactive Data Mining....Pages 1-14
Proactive Data Mining: A General Approach and Algorithmic Framework....Pages 15-20
Proactive Data Mining Using Decision Trees....Pages 21-33
Proactive Data Mining in the Real World: Case Studies....Pages 35-61
Sensitivity Analysis of Proactive Data Mining....Pages 63-85
Conclusions....Pages 87-88

✦ Subjects

Data Mining and Knowledge Discovery; Information Storage and Retrieval; Information Systems Applications (incl. Internet)