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Proactive Data Mining with Decision Trees

  • Book
  • © 2014

Overview

Authors:
  1. Haim Dahan

    1. Dep. of Industrial Engineering, Tel Aviv University, Ramat Aviv, Israel

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  2. Shahar Cohen

    1. Dep. of Industrial Engineering & Managem, Shankar College of Engineering and Desig, Ramat Gan, Israel

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  3. Lior Rokach

    1. Information Systems Engineering, Ben-Gurion University, Beer-Sheva, Israel

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  4. Oded Maimon

    1. Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, Israel

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Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Introduction to Proactive Data Mining

    • Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
    Pages 1-14

  3. Proactive Data Mining: A General Approach and Algorithmic Framework

    • Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
    Pages 15-20

  4. Proactive Data Mining Using Decision Trees

    • Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
    Pages 21-33

  5. Proactive Data Mining in the Real World: Case Studies

    • Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
    Pages 35-61

  6. Sensitivity Analysis of Proactive Data Mining

    • Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
    Pages 63-85

  7. Conclusions

    • Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon
    Pages 87-88
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Keywords

About this book

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.

Reviews

From the book reviews:

“This concise (88 page) book introduces readers to the basic concepts of proactive data mining with decision trees. … The book is very well written, easy to understand, and easy to follow. Each chapter is well organized. … The book is especially useful for practitioners who would like to get started in using data mining tools for business applications.” (Xiannong Meng, Computing Reviews, October, 2014)

Authors and Affiliations

  • Dep. of Industrial Engineering, Tel Aviv University, Ramat Aviv, Israel

    Haim Dahan

  • Dep. of Industrial Engineering & Managem, Shankar College of Engineering and Desig, Ramat Gan, Israel

    Shahar Cohen

  • Information Systems Engineering, Ben-Gurion University, Beer-Sheva, Israel

    Lior Rokach

  • Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, Israel

    Oded Maimon

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