Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

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Elsevier, 2005 M02 24 - 540 páginas

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you’ll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems.

You don’t need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system.

  • Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems
  • Helps you to understand the trade-offs implicit in various models and model architectures
  • Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction
  • Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model
  • In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem
  • Presents examples in C, C++, Java, and easy-to-understand pseudo-code
  • Extensive online component, including sample code and a complete data mining workbench
 

Páginas seleccionadas

Contenido

Fuzzy Systems
65
Evolutionary Strategies
341
Index
517
Derechos de autor

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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration
Earl Cox
Vista previa limitada - 2005

Términos y frases comunes

actual alpha cut analysis approach assigned associated Boolean Boolean logic centroid chapter chromosome cluster centers column compatibility index complete concepts constraints created crew scheduler crisp crossover data mining data point database defined defuzzification degree of membership Delta dependent distance domain duration evaluation example executed Expression feasible Figure fitness function fitness values fuzzy c-means fuzzy clustering fuzzy logic fuzzy model fuzzy number fuzzy region fuzzy rule fuzzy SQL fuzzy systems genetic algorithm genome graph hedges i-th illustrates independent variables inference engine knowledge base knowledge discovery maximum measure membership function membership values method mutation node number of fuzzy objective function optimization outcome fuzzy set outcome variable parameters partitioning patterns population prediction problem produce QCIX random relationships represent robust rule induction rule-based selected semantics shows solution space standard error statistical Table Tall techniques Temperature term set threshold topological sort variable’s vector weight zero

Acerca del autor (2005)

Earl founded and serves as President of, Scianta Intelligence, a next generation machine intelligence and knowledge exploration company. He is a futurist, author, management consultant, and educator involved in discovering the epistemology of advanced intelligent systems, the redefinition of the machine mind, and, as a pioneer of Internet-based technologies, the way in which evolving inter-connected virtual worlds will affect the sociology of business and culture in the near and far future.Earl has over thirty years experience in managing and participating in the software development process at the system as well as tightly integrated application level. In the area of advanced machine intelligence technologies, Earl is a recognized expert in fuzzy logic, and adaptive fuzzy systems as they are applied to information and decision theory. He has pioneered the integration of fuzzy neural systems with genetic algorithms and case-based reasoning. As an industry observer and futurist, Earl has written and talked extensively on the philosophy of the Response to Change, the nature of Emergent Intelligence, and the Meaning of Information Entropy in Mind and Machine.

Información bibliográfica

TítuloFuzzy Modeling and Genetic Algorithms for Data Mining and Exploration
The Morgan Kaufmann Series in Data Management Systems
AutorEarl Cox
EditorElsevier, 2005
ISBN0080470599, 9780080470597
Largo540 páginas
  
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