Data Mining Using Grammar Based Genetic Programming and Applications / Edition 1

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Data mining involves the non-trivial extraction of implicit, previousl y unknown, and potentially useful information from databases. Genetic Programming (GP) and Inductive Logic Programming (ILP) are two of the approaches for data mining. This book first sets the necessary backgro unds for the reader, including an overview of data mining, evolutionar y algorithms and inductive logic programming. It then describes a fram ework, called GGP (Generic Genetic Programming), that integrates GP an d ILP based on a formalism of logic grammars. The formalism is powerfu l enough to represent context- sensitive information and domain-depend ent knowledge. This knowledge can be used to accelerate the learning s peed and/or improve the quality of the knowledge induced.

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Editorial Reviews

Describing data mining as the non-trivial extraction of implicit, previously unknown, and potentially useful information from databases, Wong (Lingnan U., Hong Kong) and Leung (Chinese U. of Hong Kong) first review the principles behind it and behind evolutionary algorithms and inductive logic programming. Then they introduce a framework they call Generic Genetic Programming that integrates genetic and inductive programming based on a formalism of logic grammars. It is powerful enough to represent context-sensitive information and domain-dependent knowledge that can be used to accelerate the learning speed and improve the quality of the knowledge induced. They conclude by detailing their grammar-based programming system LOGENPRO and test it on many problems in data mining. Annotation c. Book News, Inc., Portland, OR (
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Product Details

  • ISBN-13: 9780792377467
  • Publisher: Springer US
  • Publication date: 2/29/2000
  • Series: Genetic Programming Series, #3
  • Edition description: 2002
  • Edition number: 1
  • Pages: 214
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.56 (d)

Table of Contents

List of Figures. List of Tables. Preface. 1. Introduction. 2. An Overview of Data Mining. 3. An Overview on Evolutionary Algorithms. 4. Inductive Logic Programming. 5. The Logic Grammars Based Genetic Programming System (LOGENPRO). 6. Data Mining Applications Using LOGENPRO. 7. Applying LOGENPRO for Rule Learning. 8. Medical Data Mining. 9. Conclusion and Future Work. Appendix A: The Rule Sets Discovered. Appendix B: The Grammar Used for the Fracture and Scoliosis Databases. References. Index.

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