On entropy-based data mining

被引：32

作者：

Holzinger, Andreas ^{[1
]}

Hörtenhuber, Matthias ^{[2
]}

Mayer, Christopher ^{[2
]}

Bachler, Martin ^{[2
]}

Wassertheurer, Siegfried ^{[2
]}

Pinho, Armando J ^{[3
]}

Koslicki, David ^{[4
]}

机构：

[1] Medical University Graz, Austria Institute for Medical Informatics, Statistics and Documentation, Research Unit Human-Computer Interaction, Graz

[2] AIT Austrian Institute of Technology GmbH, Health and Environment Department, Biomedical Systems, Donau-City-Str. 1, Vienna

[3] IEETA / Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro

[4] Oregon State University, Mathematics Department, Corvallis, OR

来源：

| 1600年 / Springer Verlag卷 / 8401期

关键词：

Approximate entropy; Biomedical informatics; Data mining; Entropy; FiniteTopEn; Fuzzy entropy; Knowledge discovery; Sample entropy; Topological entropy;

D O I：

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

中图分类号：

学科分类号：

摘要：

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-theart, 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. © Springer-Verlag Berlin Heidelberg 2014.

引用

页码：209 / 226

页数：17

共 72 条

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