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Data Mining Using Grammar Based Genetic Programming and Applications (Hardcover, 2002 ed.): Man Leung Wong, Kwong Sak Leung Data Mining Using Grammar Based Genetic Programming and Applications (Hardcover, 2002 ed.)
Man Leung Wong, Kwong Sak Leung
R4,308 Discovery Miles 43 080 Ships in 12 - 17 working days

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.

Nonlinear Integrals And Their Applications In Data Mining (Hardcover): Zhenyuan Wang, Rong Yang, Kwong Sak Leung Nonlinear Integrals And Their Applications In Data Mining (Hardcover)
Zhenyuan Wang, Rong Yang, Kwong Sak Leung
R3,307 Discovery Miles 33 070 Ships in 10 - 15 working days

Regarding the set of all feature attributes in a given database as the universal set, this monograph discusses various nonadditive set functions that describe the interaction among the contributions from feature attributes towards a considered target attribute. Then, the relevant nonlinear integrals are investigated. These integrals can be applied as aggregation tools in information fusion and data mining, such as synthetic evaluation, nonlinear multiregressions, and nonlinear classifications. Some methods of fuzzification are also introduced for nonlinear integrals such that fuzzy data can be treated and fuzzy information is retrievable.

The book is suitable as a text for graduate courses in mathematics, computer science, and information science. It is also useful to researchers in the relevant area.

Data Mining Using Grammar Based Genetic Programming and Applications (Paperback, Softcover reprint of the original 1st ed.... Data Mining Using Grammar Based Genetic Programming and Applications (Paperback, Softcover reprint of the original 1st ed. 2002)
Man Leung Wong, Kwong Sak Leung
R4,210 Discovery Miles 42 100 Ships in 10 - 15 working days

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.

Future Directions Of Fuzzy Theory And Systems (Hardcover): Kwong Sak Leung, Yeung Yam Future Directions Of Fuzzy Theory And Systems (Hardcover)
Kwong Sak Leung, Yeung Yam
R2,680 Discovery Miles 26 800 Out of stock

This monograph includes expanded selected papers presented in the "Workshop on the Future Directions of Fuzzy Theory and Systems". It contains many recent developments in the field and provides valuable insights into the future direction and applications of fuzzy theory and systems.

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