| 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. |