Proactive Data Mining with Decision Trees / by Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon.

Format:

Book

Author/Creator:

Dahan, Haim, author.

Cohen, Shahar, author.

Rokach, Lior, author.

Maimon, Oded, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

SpringerBriefs in electrical and computer engineering 2191-8112

SpringerBriefs in Electrical and Computer Engineering, 2191-8112

Language:

English

Subjects (All):

Data mining.

Information storage and retrieval.

Application software.

Data Mining and Knowledge Discovery.

Information Storage and Retrieval.

Information Systems Applications (incl. Internet).

Local Subjects:

Data Mining and Knowledge Discovery.

Information Storage and Retrieval.

Information Systems Applications (incl. Internet).

Physical Description:

1 online resource (X, 88 pages) : 20 illustrations.

Edition:

First edition 2014.

Contained In:

Springer eBooks

Place of Publication:

New York, NY : Springer New York : Imprint: Springer, 2014.

System Details:

text file PDF

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.

Contents:

Introduction

Proactive Data Mining: A General Approach

Proactive Data Mining Using Decision Trees

Proactive Data Mining in the Real World: Case Studies

Sensitivity Analysis of Proactive Data Mining

Conclusions.

Other Format:

Printed edition:

ISBN:

978-1-4939-0539-3

9781493905393

Access Restriction:

Restricted for use by site license.