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Reliable graph discovery

Dai, Honghua 2012, Reliable graph discovery. In Dai, Honghua, Liu, James N. K. and Smirnov, Evgueni (ed), Reliable knowledge discovery, Springer, New York, N. Y., pp.93-107, doi: 10.1007/978-1-4614-1903-7_5.

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Title Reliable graph discovery
Author(s) Dai, Honghua
Title of book Reliable knowledge discovery
Editor(s) Dai, Honghua
Liu, James N. K.
Smirnov, Evgueni
Publication date 2012
Chapter number 5
Total chapters 17
Start page 93
End page 107
Total pages 15
Publisher Springer
Place of Publication New York, N. Y.
Keyword(s) data mining
Summary A critical question in data mining is that can we always trust what discovered by a data mining system unconditionally? The answer is obviously not. If not, when can we trust the discovery then? What are the factors that affect the reliability of the discovery? How do they affect the reliability of the discovery? These are some interesting questions to be investigated. In this chapter we will firstly provide a definition and the measurements of reliability, and analyse the factors that affect the reliability. We then examine the impact of model complexity, weak links, varying sample sizes and the ability of different learners to the reliability of graphical model discovery. The experimental results reveal that (1) the larger sample size for the discovery, the higher reliability we will get; (2) the stronger a graph link is, the easier the discovery will be and thus the higher the reliability it can achieve; (3) the complexity of a graph also plays an important role in the discovery. The higher the complexity of a graph is, the more difficult to induce the graph and the lower reliability it would be. We also examined the performance difference of different discovery algorithms. This reveals the impact of discovery process. The experimental results show the superior reliability and robustness of MML method to standard significance tests in the recovery of graph links with small samples and weak links.
ISBN 1461419034
Language eng
DOI 10.1007/978-1-4614-1903-7_5
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890202 Application Tools and System Utilities
HERDC Research category B1 Book chapter
Copyright notice ©2012, Springer Science+Business Media, LLC
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Document type: Book Chapter
Collection: School of Information Technology
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Created: Tue, 13 Mar 2012, 09:47:07 EST

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