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Sinopsis
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.
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Críticas
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)Reseña del editor
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.
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- EditorialSpringer
- Año de publicación2014
- ISBN 10 1493905384
- ISBN 13 9781493905386
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas100
- Contacto del fabricanteno disponible
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Proactive Data Mining with Decision Trees (SpringerBriefs in Electrical and Computer Engineering)
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Proactive Data Mining with Decision Trees
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Proactive Data Mining with Decision Trees
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Springer New York Feb 2014, 2014
ISBN 10: 1493905384
ISBN 13: 9781493905386
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 100 pp. Englisch. Nº de ref. del artículo: 9781493905386
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Proactive Data Mining with Decision Trees
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Springer New York, 2014
ISBN 10: 1493905384
ISBN 13: 9781493905386
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Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Includes supplementary material: sn.pub/extrasThis 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 al. Nº de ref. del artículo: 4213828
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Proactive Data Mining with Decision Trees
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Springer New York, Springer US, 2014
ISBN 10: 1493905384
ISBN 13: 9781493905386
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9781493905386
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Proactive Data Mining with Decision Trees
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Springer New York, Springer US Feb 2014, 2014
ISBN 10: 1493905384
ISBN 13: 9781493905386
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Taschenbuch. Condición: Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 100 pp. Englisch. Nº de ref. del artículo: 9781493905386
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