Proactive Data Mining with Decision Trees

• Κύριοι συγγραφείς: Dahan, Haim (Συγγραφέας), Cohen, Shahar (Συγγραφέας), Rokach, Lior (Συγγραφέας), Maimon, Oded (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: New York, NY : Springer New York : Imprint: Springer, 2014.
• Σειρά: SpringerBriefs in Electrical and Computer Engineering,
• Θέματα: Computer science.; Data mining.; Information storage and retrieval.; Computer Science.; Data Mining and Knowledge Discovery.; Information Storage and Retrieval.; Information Systems Applications (incl. Internet).

 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. 

 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.