Proactive Data Mining with Decision Trees

Haim Dahan

ISBN 10: 1493905384 / ISBN 13: 9781493905386

Published by Springer, Springer Feb 2014, 2014

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 100 pp. Englisch.

Seller Inventory # 9781493905386

Bibliographic Details

• Title: Proactive Data Mining with Decision Trees
• Publisher: Springer, Springer Feb 2014
• Publication Date: 2014
• Language: English
• ISBN 10: 1493905384
• ISBN 13: 9781493905386
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 6D millimeters 235 millimeters width by 155 millimeters height by 6 millimeters depth
• Weight: 166 grams

About this title

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.

Review

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)