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Data Mining and Computational Intelligence : Studies in Fuzziness and Soft Computing - Abraham Kandel

Data Mining and Computational Intelligence

Studies in Fuzziness and Soft Computing

By: Abraham Kandel (Editor), Mark Last (Editor), Horst Bunke (Editor)

Hardcover

Published: 13th March 2001
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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").

Data Mining with Neuro-Fuzzy Modelsp. 1
Granular Computing in Data Miningp. 37
Fuzzification and Reduction of Information-Theoretic Rules Setsp. 63
Mining Fuzzy Association Rules in a Database Containing Relational and Transactional Datap. 95
Fuzzy Linguistic Summaries via Association Rulesp. 115
The Fuzzy-ROSA Method: A Statistically Motivated Fuzzy Approach for Data-Based Generation of Small Interpretable Rule Bases in High-Dimensional Search Spacesp. 141
Discovering Knowledge from Fuzzy Concept Latticep. 167
Mining of Labeled Incomplete Data using Fast Dimension Partitioningp. 191
Mining a Growing Feature Map by Data Skeleton Modellingp. 217
Soft Regression - A Data Mining Toolp. 251
Some Practical Applications of Soft Computing and Data Miningp. 273
Intelligent Mining in Image Databases, With Applications to Satellite Imaging and to Web Searchp. 309
Fuzzy Genetic Modeling and Forecasting for Nonlinear Time Seriesp. 337
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9783790813715
ISBN-10: 3790813710
Series: Studies in Fuzziness and Soft Computing
Audience: General
Format: Hardcover
Language: English
Number Of Pages: 356
Published: 13th March 2001
Publisher: Springer-Verlag Berlin and Heidelberg Gmbh & Co. Kg
Country of Publication: DE
Dimensions (cm): 23.5 x 15.5  x 1.91
Weight (kg): 1.54