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.
