Information mining
Due to modern information technology, which produces ever more powerful computers every year, it is possible today to collect, store, transfer, and combine huge amounts of data at very low costs. Thus an ever-increasing number of companies and scientific and governmental institutions can afford to build up large archives of documents and other data like numbers, tables, images, and sounds. However, exploiting the information contained in these archives in an intelligent way turns out to be fairly difficult. Although a user often has a vague understanding of his data and can usually formulate hypotheses and guess dependencies, he rarely knows: where to find the ''interesting'' or ''relevant'' pieces of information, whether these pieces of information support his hypotheses and models, whether (other) interesting phenomena are hidden in the data, which methods are best suited to find the needed pieces of information in a fast and reliable way, and how the data can be translated into human notions that are appropriate for the context in which they are needed.
In reply to these challenges a new area of research has emerged, called ''knowledge discovery in databases'' or ''data mining'': Knowledge discovery in databases (KDD) is a research area that considers the analysis of large databases in order to identify valid, useful, meaningful, unknown, and unexpected relationships.