Data Mining for Design and Manufacturing: Methods and ApplicationsD. Braha Data Mining for Design and Manufacturing: Methods and Applications is the first book that brings together research and applications for data mining within design and manufacturing. The aim of the book is 1) to clarify the integration of data mining in engineering design and manufacturing, 2) to present a wide range of domains to which data mining can be applied, 3) to demonstrate the essential need for symbiotic collaboration of expertise in design and manufacturing, data mining, and information technology, and 4) to illustrate how to overcome central problems in design and manufacturing environments. The book also presents formal tools required to extract valuable information from design and manufacturing data, and facilitates interdisciplinary problem solving for enhanced decision making. Audience: The book is aimed at both academic and practising audiences. It can serve as a reference or textbook for senior or graduate level students in Engineering, Computer, and Management Sciences who are interested in data mining technologies. The book will be useful for practitioners interested in utilizing data mining techniques in design and manufacturing as well as for computer software developers engaged in developing data mining tools. |
Contents
A Survey of Methodologies and Techniques for Data | 41 |
DATA MINING IN PRODUCT DESIGN | 60 |
Learning to Set Up Numerical Optimizations of | 87 |
Automatic Classification and Creation of 127 | 126 |
Data Mining for Knowledge Acquisition in | 145 |
A Data MiningBased Engineering Design Support | 161 |
Data Mining for High Quality and Quick Response | 179 |
Data Mining for Process and Quality Control in the 207 | 206 |
Derivation of Decision Rules for the Evaluation of | 337 |
An Evaluation of Sampling Methods for Data Mining 355 | 354 |
Colour Space Mining for Industrial Monitoring | 371 |
NonTraditional Applications of Data Mining | 401 |
FuzzyNeuralGenetic Layered MultiAgent Reactive | 417 |
MethodSpecific Knowledge Compilation | 443 |
A Study of Technical Challenges in Relocation of a 465 | 464 |
Using Imprecise Analogical Reasoning to Refine the | 487 |
Other editions - View all
Data Mining for Design and Manufacturing: Methods and Applications D. Braha No preview available - 2010 |
Data Mining for Design and Manufacturing: Methods and Applications D. Braha No preview available - 2001 |
Common terms and phrases
accuracy application approach approximation Artificial Intelligence behavior case-based reasoning chip locations cluster components Computer conditional entropy constraint cost data mining process data set data warehouse database decision tree defined Design and Manufacturing design goal design process developed dimensional dimensionless distribution domain Engineering Design entropy error rate evaluation experiments extracted Fayyad formulation selection function fuzzy fuzzy sets genetic algorithms imprecise inductive learning input attributes integration Knowledge Discovery knowledge systems layer linear machine learning mapping matrix measure methodology monitoring mutual information neural networks neurons node objects OLAP operation optimization output pattern recognition performance prediction problem procedure process variables prototype prototype-selection query random reduced regression relevant robot rough set rules sampling method search space shown in Figure similarity simulator stage statistical structure Table target attribute training data training examples transformation tuples unsupervised learning vector wafer wavelet