Items related to Data Mining and Computational Intelligence (Studies...
Data Mining and Computational Intelligence (Studies in Fuzziness and Soft Computing, Volume 68) - Hardcover
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").
"synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About this title" may belong to another edition of this title.
- PublisherPhysica
- Publication date2001
- ISBN 10 3790813710
- ISBN 13 9783790813715
- BindingHardcover
- Number of pages368
- EditorKandel Abraham, Last Mark, Bunke Horst
Buy New
Learn more about this copy
US$ 169.72
Shipping:
FREE
Within U.S.A.
Top Search Results from the AbeBooks Marketplace
Seller Image
Data Mining and Computational Intelligence.
Seller: Antiquariat Dorner, Reinheim, Germany
Seller rating 5 out of 5 stars
Condition: UNSPECIFIED. Studies in Fuzziness and Soft Computing 68. Heidelberg, Physica Verlag 2001. XII, 356 S., OPappband Sehr gutes Exemplar. Seller Inventory # 119854
Quantity: 1 available
Seller Image
Data Mining and Computational Intelligence (Studies in Fuzziness and Soft Computing, Volume 68) [Hardcover ]
Seller: booksXpress, Bayonne, NJ, U.S.A.
Seller rating 4 out of 5 stars
Hardcover. Condition: NEW. Seller Inventory # 9783790813715
Quantity: 10 available
Seller Image
Data Mining and Computational Intelligence
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: NEW. Seller Inventory # 920775-n
Quantity: 5 available
Stock Image
Data Mining and Computational Intelligence
Seller: Buchpark, Trebbin, Germany
Seller rating 5 out of 5 stars
Condition: USED_FINE. Zustand: Sehr gut - Gepflegter, sauberer Zustand. Außen: angestoßen. Aus der Auflösung einer renommierten Bibliothek. Kann Stempel beinhalten. | Seiten: 372 | Sprache: Englisch | Produktart: Bücher. Seller Inventory # 213287/202
Quantity: 1 available
Stock Image
Data Mining and Computational Intelligence (Studies in Fuzziness and Soft Computing, Volume 68)
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Condition: NEW. Seller Inventory # ABLIING23Apr0316110061155
Quantity: Over 20 available
Stock Image
Data Mining and Computational Intelligence
Print on DemandSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: NEW. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. Seller Inventory # ria9783790813715_lsuk
Quantity: Over 20 available
Seller Image
Data Mining and Computational Intelligence
Seller: GreatBookPricesUK, Castle Donington, DERBY, United Kingdom
Seller rating 5 out of 5 stars
Condition: NEW. Seller Inventory # 920775-n
Quantity: 5 available
Seller Image
Data Mining and Computational Intelligence
Published by
Physica-Verlag, Physica-Verlag HD, Physica Mrz 2001, 2001
ISBN 10: 3790813710
ISBN 13: 9783790813715
New
Hardcover
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: NEW. 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. Seller Inventory # 9783790813715
Quantity: 2 available
Seller Image
Data Mining and Computational Intelligence
Print on DemandSeller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: 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. Seller Inventory # 5310333
Quantity: Over 20 available
Seller Image
Data Mining and Computational Intelligence
Print on DemandSeller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Buch. Condition: NEW. nach der Bestellung gedruckt Neuware - Printed after ordering - 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'). Seller Inventory # 9783790813715
Quantity: 1 available