Nowadays data accumulate at an alarming speed in various storage devices,
and so does valuable information. However, it is difficult to understand information
hidden in data without the aid of data analysis techniques, which
has provoked extensive interest in developing a field separate from machine
learning. This new field is data mining.
Data mining has successfully provided solutions for finding information
from data in bioinformatics, pharmaceuticals, banking, retail, sports and entertainment,
etc. It has been one of the fastest growing fields in the computer
industry. Many important problems in science and industry have been addressed
by data mining methods, such as neural networks, fuzzy logic, decision
trees, genetic algorithms, and statistical methods.
This book systematically presents how to utilize fuzzy neural networks,
multi-layer perceptron (MLP) neural networks, radial basis function (RBF)
neural networks, genetic algorithms (GAs), and support vector machines
(SVMs) in data mining tasks. Fuzzy logic mimics the imprecise way of reasoning
in natural languages and is capable of tolerating uncertainty and vagueness.
The MLP is perhaps the most popular type of neural network used
today. The RBF neural network has been attracting great interest because
of its locally tuned response in RBF neurons like biological neurons and its
global approximation capability. This book demonstrates the power of GAs in
feature selection and rule extraction. SVMs are well known for their excellent
accuracy and generalization abilities.
We will describe data mining systems which are composed of data preprocessing,
knowledge-discovery models, and a data-concept description. This
monograph will enable both new and experienced data miners to improve their
practices at every step of data mining model design and implementation.