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INTRODUCTION
One aspect of our technological society is clear — there is a large amount of data but a shortage of information. Every day, enormous amounts of information are generated from all sectors — business, education, the scientific community, the World Wide Web, or one of many off-line and online data sources readily available. From all of this, which represents a sizable repository of human data and information, it is necessary and desirable to generate worthwhile and usable knowledge. As a result, the field of data mining and knowledge discovery in databases (KDD) has grown in leaps and bounds, and has shown great potential for the future (Han & Kamber, 2001). Data mining is not a single technique or technology but, rather, a group of related methods and methodologies that are directed towards the finding and automatic extraction of patterns, associations, changes, anomalies, and significant structures from data (Grossman, 1998). Data mining is emerging as a key technology that enables businesses to select, filter, screen, and correlate data automatically. Data mining evokes the image of patterns and meaning in data, hence the term that suggests the mining of “nuggets” of knowledge and insight from a group of data. The findings from these can then be applied to a variety of applications and purposes, including those in marketing, risk analysis and management, fraud detection and management, and customer relationship management (CRM). With the considerable amount of information that is being generated and made available, the effective use of data-mining methods and techniques can help to uncover various trends, patterns, inferences, and other relations from the data, which can then be analyzed and further refined. These can then be studied to bring out meaningful information that can be used to come to important conclusions, improve marketing and CRM efforts, and predict future behavior and trends (Han & Kamber, 2001).
