Abstract
Over the years, the term data mining has been connected to various types of analytical approaches. In fact, just a few years ago, let’s say prior to 1995, many individuals in the software industry and business users as well, often referred to OLAP as a main component of data mining technology. More recently however, this term has taken on a new meaning and one which will most likely prevail for years to come. As we mentioned in the previous chapter, data mining technology encompasses such methodologies as clustering, classification and segmentation, association, neural networks and regression as the main players in this space. Other analytical processes which are related to mining, as defined in this work, include such methodologies as Linear Programming, Monte Carlo analysis and Bayesian methodologies. In fact, depending on who you ask, these techniques may actually be considered part of the data mining spectrum since they are grounded in mathematical techniques applied to historical data. The focus of this work however, revolves around the former more core approaches. Regardless of the type of methodology, data mining has taken its roots from traditional analytical techniques. Enhancements in computer processing, (e.g., speed and processing power) has enabled a wider diffusion of more complex techniques to become more automated and user friendly and have evolved to the state of our current data mining.