Introduction

Abstract

Database mining seeks to extract previously unrecognized information from data stored in conventional databases. Database mining has also been called database exploration and Knowledge Discovery in Databases (KDD). Databases have significant amount of stored data. This data continues to grow exponentially. Much of the data is implicitly or explicitly imprecise. The data is valuable because it is collected to explicitly support particular enterprise activities. There could be valuable, undiscovered relationships in the data. A human analyst can be overwhelmed by the glut of digital information. New technologies and their application are required to overcome information overload. Database discovery seeks to discover noteworthy, unrecognized associations between data items in an existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. A metaphor for database discovery is mining. Database mining elicits knowledge that is implicit in the databases. The rate at which the data is stored is growing at a phenomenal rate. As a result, traditional ad hoc mixtures of statistical techniques and data management tools are no longer adequate for analyzing this vast collection of data [1]. Several domains where large volumes of data are stored in centralized or distributed databases include the following applications in electronic commerce, bioinformatics, computer security, Web intelligence, intelligent learning database systems, finance, marketing, healthcare, telecommunications, and other fields, which can be broadly classified as, 1. Financial Investment: Stock indexes and prices, interest rates, credit card data, fraud detection. 2. Health Care: Several diagnostic informationstored by hospital management systems. 3. Manufacturing and Production: Process optimization and troubleshooting. 4. Telecommunication Network: Calling patterns and fault management systems. 5. Scientific Domain: Astronomical observations, genomic data, biological data. 6. The World Wide Web. The area of Data Mining encompasses techniques facilitating the extraction of knowledge from large amount of data. These techniques include topics such as pattern recognition, machine learning, statistics, database tools and On-Line Analytical Processing (OLAP). Data mining is one part of a larger process referred to as Knowledge Discovery in Database (KDD). The KDD process is comprised of the following steps: (i) Data Cleaning (ii) Data Integration (iii) Data Selection (iv) Data Transformation (v) Data Mining (vi) Pattern Evaluation (vii) Knowledge Presentation. © 2009 Springer-Verlag Berlin Heidelberg.