Data mining and knowledge discovery is the principle of analyzing large amounts of data and picking out relevant information leading to the knowledge discovery process for extracting meaningful patterns, rules, and models from raw data making discovered patterns understandable. Applications include medicine, politics, games, business, marketing, bioinformatics, and many other areas of science and engineering. It is an area of research activity that stands at the intellectual intersection of statistics, computer science, machine learning, and database management. It deals with very large data sets, tries to make fewer theoretical assumptions than has traditionally been done in statistics, and typically focuses on problems of classification, prediction, description and profiling, clustering, and regression. In such domains, data mining often uses decision trees or neural networks as models and frequently fits them using some combination of techniques such as bagging, boosting/arcing,...
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Kokol, P. (2015). Data Mining and Knowledge Discovery, Introduction to. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-3-642-27737-5_115-2
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DOI: https://doi.org/10.1007/978-3-642-27737-5_115-2
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Publisher Name: Springer, New York, NY
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