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Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

Volume 8401 of the series Lecture Notes in Computer Science pp 209-226

On Entropy-Based Data Mining

  • Andreas HolzingerAffiliated withInstitute for Medical Informatics, Statistics & Documentation, Research Unit Human-Computer Interaction, Medical University Graz
  • , Matthias HörtenhuberAffiliated withHealth & Environment Department, Biomedical Systems, AIT Austrian Institute of Technology GmbH
  • , Christopher MayerAffiliated withHealth & Environment Department, Biomedical Systems, AIT Austrian Institute of Technology GmbH
  • , Martin BachlerAffiliated withHealth & Environment Department, Biomedical Systems, AIT Austrian Institute of Technology GmbH
  • , Siegfried WassertheurerAffiliated withHealth & Environment Department, Biomedical Systems, AIT Austrian Institute of Technology GmbH
  • , Armando J. PinhoAffiliated withIEETA / Department of Electronics, Telecommunications and Informatics, University of Aveiro
  • , David KoslickiAffiliated withMathematics Department, Oregon State University

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Abstract

In the real world, we are confronted not only with complex and high-dimensional data sets, but usually with noisy, incomplete and uncertain data, where the application of traditional methods of knowledge discovery and data mining always entail the danger of modeling artifacts. Originally, information entropy was introduced by Shannon (1949), as a measure of uncertainty in the data. But up to the present, there have emerged many different types of entropy methods with a large number of different purposes and possible application areas. In this paper, we briefly discuss the applicability of entropy methods for the use in knowledge discovery and data mining, with particular emphasis on biomedical data. We present a very short overview of the state-of-the-art, with focus on four methods: Approximate Entropy (ApEn), Sample Entropy (SampEn), Fuzzy Entropy (FuzzyEn), and Topological Entropy (FiniteTopEn). Finally, we discuss some open problems and future research challenges.

Keywords

Entropy Data Mining Knowledge Discovery Topological Entropy FiniteTopEn Approximate Entropy Fuzzy Entropy Sample Entropy Biomedical Informatics