ABSTRACT
Contents 20.1 Introduction ............................................................................................ 448 20.2 Clustering Method .................................................................................. 448 20.3 Rule-Induction Method ...........................................................................450 20.4 Artificial Neural Network Method...........................................................451 20.5 Nearest Neighbour Method .....................................................................453 20.6 Decision Tree Method ..............................................................................454 20.7 Statistical Method ....................................................................................454 20.8 Applications and Conclusions ..................................................................456 References .........................................................................................................457
20.1 Introduction The large amount of data stored in a number of organisations provides means for extracting essential information or trends that are not obvious. As such, the science of data mining arises to crunch the enormous amount of data for business and technological applications [1]. It follows that data mining methodologies emerge from the storage of business database and progress in data availability, which allow ready information retrieval that paves the way for users to sift through the data in real time. As a result of expansion in business intelligence, data mining has been adopted in marketing, fraud detection, surveillance, and technological advances. Data mining has been defined as a process of extracting useful patterns and trends from large amounts of data, or the automated retrieval of predictive information that is not obvious from large databases.
