Computational Complexity

2012 Edition
| Editors: Robert A. Meyers (Editor-in-Chief)

Data-Mining and Knowledge Discovery, Introduction to

  • Peter Kokol
Reference work entry

Data mining and knowledge discovery is the principle of analyzing large amounts of data and picking out relevantinformation leading to the knowledge discovery process for extracting meaningful patterns, rules and models from raw data making discovered patternsunderstandable. Applications include medicine, politics, games, business, marketing, bioinformatics and many other areas of science and engineering. It isan area of research activity that stands at the intellectual intersection of statistics, computer science, machine learning and database management. Itdeals with very large datasets, tries to make fewer theoretical assumptions than has traditionally been done in statistics, and typically focuses onproblems of classification, prediction, description and profiling, clustering, and regression. In such domains, data mining often uses decision trees orneural networks as models and frequently fits them using some combination of techniques such as bagging, boosting/arcing, and racing....


Data Mining Knowledge Discovery Ensemble Method Discovery System System Process Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Peter Kokol
    • 1
  1. 1.Department of Computer ScienceUniversity of MariborMariborSlovenia