Data mining deals with search for description especially in large databases.
Basically, this search is meant for certain information elicitation with the
presence of others. More explicitly, data mining refers to variety of techniques to
extract information by identi@ing relationships and global patterns that exist in
large databases. Such information is mostly obscured among the vast amount of
data. Since the data around the information of interest is considered to be
irrelevant, they are effectively termed to be noise, which are practically desired
to be absent. In this noisy environment, there are many diverse methods, which
explain relationships or identi& patterns in the data set. Among these methods
reference may be made to cluster analysis, discriminant analysis, regression
analysis, principal component analysis, hypothesis testing, modeling and so
forth.
The majority of the data mining methods fall in the category of statistics.
What makes the data mining methods different is the source and amount of the
data. Namely, the source is a database, which supposedly has a big amount of
relevant and irrelevant data in suitable and/or unsuitable form for the intended
information to be extracted. Referring to the large size of the data set, the
conventional statistical approaches may not be conclusive. This is because of the
complexity of the data where only few is known about the properties so that it
can neither be treated in a statistical framework nor in a deterministic modeling
framework, for instance. A large data set ffom a stock market is a good example
where apparently there is no established physical process behind so that the
properties and behavior can only be modeled in a non-parametric form. The
validation of such a model is subject to elaborated investigations.
The main feature of a data set subject to mining is complexity and the
characteristic feature of the data mining methods distinguishes themselves by
“learning” as to the conventional statistical methods. That is, even the wellknown 
statistical or non-statistical or alternatively parametric or non-parametric
methods are used, the final model parameters or feature vectors of concern are
established partly or filly by learning. Note that, in conventional statistical
modeling for relationship or pattern identification, model or pattern parameters
are established by statistical computation in contrast with learning in data mining
exercise. Although statistical techniques are apparently ubiquitous in data
mining, data mining should not be carried out with statistics unless this is
justified. Statistical methods assist the user in preparation the data for mining.
This assistance might be in the form of data reduction and hypothesis forming.
Such a preparation is especially beneficial for knowledge discovery by soft
computing following the information extraction by data mining. In this work,
learning in soft computing is accomplished by machine learning methods