Biological Data Mining

Biological Data Mining

George Tzanis (Aristotle University of Thessaloniki, Greece), Christos Berberidis (Aristotle University of Thessaloniki, Greece) and Ioannis Vlahavas (Aristotle University of Thessaloniki, Greece)
Copyright: © 2005 |Pages: 7
DOI: 10.4018/978-1-59140-560-3.ch007
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Abstract

At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).

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