Databases and Knowledge Discovery

Data Mining has evolved into a mainstream technology because of two complementary, yet antagonistic phenomena:

1. the data deluge, fueled by the maturing of database technology and the development of advanced automated data collection tools and,
2. the starvation for knowledge, defined as the need to filter and interpret all these massive data volumes stored in databases, data warehouses and other information repositories.

In contrast to preceding technologies, Data Mining techniques are computer-driven, therefore can be fully automated and they confront the visualization and understanding of large data sets efficiently. These factors, coupled with the rapid development of new and improved databases have made DM technology nowadays an integral part of information systems.

Data Mining is closely related to Knowledge Discovery and quite often these two processes are considered equivalent. Widely accepted definitions for these technologies are:

Knowledge Discovery in Databases (KDD) is the process of extracting interesting, non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases.

Data Mining is the most important step in the KDD process and involves the application of data analysis and discovery algorithms that, under acceptable computational efficiency limitations, produce a particular enumeration of patterns over the data.

Data Mining techniques vary from Classification and Clustering analysis, to Association Rule Extraction and Deviation Detection. The results of data mining analysis are widely popular to many application domains, namely Retail, Telecommunications, Banking, Fraud analysis, DNA mining, Stock market analysis, Web mining, Weblog analysis, E-commerce, etc.

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