Data Mining and Computational Intelligence

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

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Hardcover | 13 March 2001

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Hardcover
372 Pages

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23.39 x 15.6 x 2.24

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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").

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Computer Science Artificial Intelligence Expert Systems & Knowledge-Based Systems Business & Management Business Mathematics & Systems

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

Data Mining and Computational Intelligence

At a Glance

Hardcover

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How to return your order

Defective items

You Can Find This Book In

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