Proactive data mining with decision trees

The Resource Proactive data mining with decision trees

Label

Proactive data mining with decision trees

Statement of responsibility

Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon

Language

eng

Summary

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

Cataloging source

GW5XE

Dewey number

006.312

Illustrations

illustrations

Index

no index present

LC call number

QA76.9.D343

Literary form

non fiction

Nature of contents

dictionaries
bibliography

Series statement

SpringerBriefs in Electrical and Computer Engineering,

Context

Context of Proactive data mining with decision trees

Structured data from the Bibframe namespace is licensed under the Creative Commons Attribution 4.0 International License by University of Missouri Libraries. Additional terms may apply to data associated with third party namespaces.

Context

Work of