On Entropy-Based Data Mining

Andreas Holzinger; Matthias Hörtenhuber; Christopher Mayer; Martin Bachler; Siegfried Wassertheurer; Armando Pinho; David Koslicki

doi:10.1007/978-3-662-43968-5_12

On Entropy-Based Data Mining

Andreas Holzinger, Matthias Hörtenhuber, Christopher Mayer, Martin Bachler, Siegfried Wassertheurer, Armando Pinho, David Koslicki

Institute of Interactive Systems and Data Science (7060)

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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.

Original language	English
Title of host publication	Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, LNCS 8401
Place of Publication	Heidelberg, Berlin, New York
Publisher	Springer
Pages	209-226
Volume	8401
Edition	1
ISBN (Print)	978-3-662-43967-8
DOIs	https://doi.org/10.1007/978-3-662-43968-5_12
Publication status	Published - 2014

Keywords

Information Entropy
Data Mining
Health Informatics
Knowledge Discovery
Topological Entropy

ASJC Scopus subject areas

Computational Theory and Mathematics

Fields of Expertise

Information, Communication & Computing

Treatment code (Nähere Zuordnung)

Basic - Fundamental (Grundlagenforschung)
Application

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10.1007/978-3-662-43968-5_12

HOLZINGER-Entropy-based-data-miningFinal published version, 913 KB

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AU - Holzinger, Andreas

AU - Hörtenhuber, Matthias

AU - Mayer, Christopher

AU - Bachler, Martin

AU - Wassertheurer, Siegfried

AU - Pinho, Armando

AU - Koslicki, David

PY - 2014

Y1 - 2014

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KW - Information Entropy

KW - Data Mining

KW - Health Informatics

KW - Knowledge Discovery

KW - Topological Entropy

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M3 - Chapter

SN - 978-3-662-43967-8

VL - 8401

SP - 209

EP - 226

BT - Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, LNCS 8401

PB - Springer

CY - Heidelberg, Berlin, New York

ER -

On Entropy-Based Data Mining

Abstract

Keywords

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

Access to Document

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Selection of entropy-measure parameters for knowledge discovery in heart rate variability data

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