Special section: Data mining in grid computing environments

摘要

Data mining can be viewed as the formulation, analysis, and implementation of an induction process proceeding from specific data to general patterns that facilitates the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Data mining ranges from highly theoretical mathematical work in areas like statistics, machine learning, knowledge representation, and algorithms to systems solutions for problems like fraud detection, modeling of cancer and other complex diseases, network intrusion, information retrieval on the Web, and monitoring of grid systems. Data mining techniques are increasingly employed in traditional scientific discovery disciplines, such as biological, medical, biomedical, chemical, physical, and social research, and a variety of other knowledge industries, such as government, education, high-tech engineering, and process automation. Thus, data mining is playing an increasingly important role in structuring and shaping future knowledge-based industries and businesses. The effective and efficient management and use of stored data, and in particular the transformation of these data into information and knowledge, is considered a key requirement for success in such domains.