Data Mining Using Grammar Based Genetic Programming and Applications

1. Front Matter

   Pages i-xiv

   PDF

2. Introduction

   Pages 1-8

3. An Overview of Data Mining

   Pages 9-25

4. An Overview on Evolutionary Algorithms

   Pages 27-55

5. Inductive Logic Programming

   Pages 57-69

6. The Logic Grammars Based Genetic Programming System (LOGENPRO)

   Pages 71-100

7. Data Mining Applications Using LOGENPRO

   Pages 101-136

8. Applying LOGENPRO for Rule Learning

   Pages 137-160

9. Medical Data Mining

   Pages 161-167

10. Conclusion and Future Work

    Pages 169-175

11. Back Matter

    Pages 177-213

    PDF

Data mining involves the non-trivial extraction of implicit, previously 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 backgrounds for the reader, including an overview of data mining, evolutionary algorithms and inductive logic programming. It then describes a framework, called GGP (Generic Genetic Programming), that integrates GP and ILP based on a formalism of logic grammars. The formalism is powerful enough to represent context- sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the knowledge induced.
A grammar-based genetic programming system called LOGENPRO (The LOGic grammar based GENetic PROgramming system) is detailed and tested on many problems in data mining. It is found that LOGENPRO outperforms some ILP systems. We have also illustrated how to apply LOGENPRO to emulate Automatically Defined Functions (ADFs) to discover problem representation primitives automatically. By employing various knowledge about the problem being solved, LOGENPRO can find a solution much faster than ADFs and the computation required by LOGENPRO is much smaller than that of ADFs. Moreover, LOGENPRO can emulate the effects of Strongly Type Genetic Programming and ADFs simultaneously and effortlessly.
Data Mining Using Grammar Based Genetic Programming and Applications is appropriate for researchers, practitioners and clinicians interested in genetic programming, data mining, and the extraction of data from databases.

• data mining
• database
• evolution
• evolutionary algorithm
• genetic programming
• grammar
• knowledge
• learning
• logic programming

• Lingnan University, Hong Kong

  Man Leung Wong

• The Chinese University of Hong Kong, Hong Kong

  Kwong Sak Leung

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