Articles liés à Data Mining and Computational Intelligence
Synopsis
Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The idea is to utilize the potential similarity between the patterns of the past (e.g., "most students used to be profitable") and the patterns of the future (e.g., "students will be profitable").
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Studies in Fuzziness and Soft Computing: Data Mining and Computational Intelligence
Edité par
Physica-Verlag, 2001
ISBN 10 : 3790813710
ISBN 13 : 9783790813715
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Couverture rigide
Vendeur : Anybook.com, Lincoln, Royaume-Uni
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Etat : Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,700grams, ISBN:9783790813715. N° de réf. du vendeur 5774259
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Data Mining and Computational Intelligence
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Etat : New. N° de réf. du vendeur 920775-n
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Data Mining and Computational Intelligence (Studies in Fuzziness and Soft Computing, Volume 68)
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Etat : New. N° de réf. du vendeur ABLIING23Apr0316110061155
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Data Mining and Computational Intelligence (Hardcover)
Edité par
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Heidelberg, 2001
ISBN 10 : 3790813710
ISBN 13 : 9783790813715
Neuf
Couverture rigide
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
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Hardcover. Etat : new. Hardcover. The volume offers a comprehensive coverage of the recent advances in the application of soft computing and fuzzy logic theory to data mining and knowledge discovery databases. It focuses on some of the hardest, and yet unsolved, issues of data mining like understandability of patterns, finding complex relationships between attributes, handling missing and noisy data, mining very large datasets, change detection in time series, and integration of the discovery process with database management systems. Many business decisions are made in the absence of complete information about the decision consequences. The idea is to utilize the potential similarity between the patterns of the past (e.g., "most students used to be profitable") and the patterns of the future (e.g., "students will be profitable"). Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783790813715
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Data Mining and Computational Intelligence (Studies in Fuzziness and Soft Computing, Volume 68)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Etat : New. In. N° de réf. du vendeur ria9783790813715_new
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Data Mining and Computational Intelligence
Edité par
Physica-Verlag, Physica-Verlag HD, Physica Mrz 2001, 2001
ISBN 10 : 3790813710
ISBN 13 : 9783790813715
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The idea is to utilize the potential similarity between the patterns of the past (e.g., 'most students used to be profitable') and the patterns of the future (e.g., 'students will be profitable'). 356 pp. Englisch. N° de réf. du vendeur 9783790813715
Quantité disponible : 2 disponible(s)
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Data Mining and Computational Intelligence
Edité par
Physica-Verlag HD, 2001
ISBN 10 : 3790813710
ISBN 13 : 9783790813715
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Comprehensive coverage of recent advances in the application of soft computing and fuzzy logic data miningAlso useful as a reference book in data mining, machine learning, fuzzy logic, and artificial intelligenceComprehensive coverage of recent a. N° de réf. du vendeur 5310333
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Data Mining and Computational Intelligence
Edité par
Physica-Verlag HD, 2001
ISBN 10 : 3790813710
ISBN 13 : 9783790813715
Ancien ou d'occasion
Couverture rigide
Vendeur : Buchpark, Trebbin, Allemagne
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Etat : Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher. N° de réf. du vendeur 213287/202
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Data Mining and Computational Intelligence
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Etat : New. pp. 372. N° de réf. du vendeur 26553909
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Data Mining and Computational Intelligence
Edité par
Physica-Verlag HD Mär 2001, 2001
ISBN 10 : 3790813710
ISBN 13 : 9783790813715
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Neuware -Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The idea is to utilize the potential similarity between the patterns of the past (e.g., 'most students used to be profitable') and the patterns of the future (e.g., 'students will be profitable').Physica Verlag, Tiergartenstr. 17, 69121 Heidelberg 372 pp. Englisch. N° de réf. du vendeur 9783790813715
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