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Record Nr. |
UNINA9910298557703321 |
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Autore |
Kaufmann Michael |
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Titolo |
Inductive Fuzzy Classification in Marketing Analytics / / by Michael Kaufmann |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
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ISBN |
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Edizione |
[1st ed. 2014.] |
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Descrizione fisica |
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1 online resource (143 p.) |
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Collana |
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Fuzzy Management Methods, , 2196-4130 |
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Disciplina |
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Soggetti |
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Information technology |
Business—Data processing |
Data mining |
Marketing |
Mathematical logic |
Application software |
E-commerce |
IT in Business |
Data Mining and Knowledge Discovery |
Mathematical Logic and Formal Languages |
Information Systems Applications (incl. Internet) |
e-Commerce/e-business |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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A Gradual Concept of Truth -- Fuzziness and Induction -- Analytics and Marketing -- Prototyping and Evaluation -- Precisiating Fuzziness by Induction. |
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Sommario/riassunto |
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To enhance marketing analytics, approximate and inductive reasoning can be applied to handle uncertainty in individual marketing models. This book demonstrates the use of fuzzy logic for classification and segmentation in marketing campaigns. Based on practical experience as a data analyst and on theoretical studies as a researcher, the author explains fuzzy classification, inductive logic, and the concept of likelihood, and introduces a blend of Bayesian and Fuzzy Set |
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approaches, allowing reasonings on fuzzy sets that are derived by inductive logic. By application of this theory, the book guides the reader towards a gradual segmentation of customers which can enhance return on targeted marketing campaigns. The algorithms presented can be used for visualization, selection and prediction. The book shows how fuzzy logic can complement customer analytics by introducing fuzzy target groups. This book is for researchers, analytics professionals, data miners and students interested in fuzzy classification for marketing analytics. |
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