Abstract
The convergence of computing and communication has resulted in a society that feeds on
information. There is exponentially increasing huge amount of information locked up in
databases—information that is potentially important but has not yet been discovered or
articulated (Whitten & Frank, 2005). Data mining, the extraction of implicit, previously
unknown, and potentially useful information from data, can be viewed as a result of the
natural evolution of Information Technology (IT). An evolutionary path has been passed in
database field from data collection and database creation to data management, data analysis
and understanding. According to Han & Camber (2001) the major reason that data mining
has attracted a great deal of attention in information industry in recent years is due to the
wide availability of huge amounts of data and the imminent need for turning such data into
useful information and knowledge. The information and knowledge gained can be used for
applications ranging from business management, production control, and market analysis,
to engineering design and science exploration. In other words, in today’s business
environment, it is essential to mine vast volumes of data for extracting patterns in order to
support superior decision-making. Therefore, the importance of data mining is becoming
increasingly obvious. Many data mining techniques have also been presented in various
applications, such as association rule mining, sequential pattern mining, classification,
clustering, and other statistical methods (Chen & Weng, 2008).