Introduction

The amount of electronically available data has grown rapidly because of increase in use of electronic data gathering devices, e.g., point-of-sale, remote sensing devices etc., and because data storage has become easier and cheaper with increasing computing power and disk storage capacity.

Data base management systems (DBMSs) have given access to the data stored but they give no analysis of the data. Analysis is required to reveal the hidden relationships within the data, for instance, for decision support. Size of databases has increased and therefore there is a strong need for automated techniques for automated analysis. The solution is data mining that has been defined as:

• the non-trivial extraction of implicit, previously unknown, and potentially useful information from data. (William J Frawley, Gregory Piatetsky-Shapiro and Christopher J. Matheus)
• a variety of techniques to identify nuggets of information or decision-making knowledge in bodies of data, and extracting these in such a way that they can be put to use in the areas such as decision support, prediction, forecasting and estimation. The data is often voluminous, but as it stands of low value as no direct use can be made of it; it is the hidden information in the data that is useful. [])

Data mining has many synonyms and related areas of research. One of the most popular alternatives for naming the area is Knowledge Discovery in Databases (KDD). In the list of frequently asked questions [] KDD is characterized as follows:

• The notion of Knowledge Discovery in Databases (KDD) has been given various names, including data mining, knowledge extraction, data pattern processing, data archaeology, information harvesting, siftware, and even (when done poorly) data dredging. Whatever the name, the essence of KDD is non-trivial process of identifying (1) valid, (2) novel, (3) potentially useful, and (4) ultimately understandable patterns in data. []

The structure of this chapter is partly based on a WWW course material prepared in the Queen's University of Belfast []. For introductory texts on the topic see, e.g., [,,].