Reliable knowledge discovery with a minimal causal model inducer

Dai, Honghua, Keble-Johnston, Sarah and Gan, Min 2012, Reliable knowledge discovery with a minimal causal model inducer, in ICDMW 2012 : Proceedings of the 12th IEEE International Conference on Data Mining Workshops, IEEE, Piscataway, N.J., pp. 629-634.

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Title Reliable knowledge discovery with a minimal causal model inducer
Author(s) Dai, Honghua
Keble-Johnston, Sarah
Gan, Min
Conference name Data Mining Workshop. Conference (12th : 2012 : Brussels, Belgium)
Conference location Brussels, Belgium
Conference dates 10 Dec. 2012
Title of proceedings ICDMW 2012 : Proceedings of the 12th IEEE International Conference on Data Mining Workshops
Editor(s) Vreeken, Jilles
Ling, Charles
Zaki, Mohammed J.
Siebes, Arno
Xu, Yu Jeffrey
Goethals, Bart
Webb, Geoff
Wu, Xindong
Publication date 2012
Conference series Data Mining Workshop Conference
Start page 629
End page 634
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) data mining
minimal model learner
reliability
Summary This paper presents a Minimal Causal Model Inducer that can be used for the reliable knowledge discovery. The minimal-model semantics of causal discovery is an essential concept for the identification of a best fitting model in the sense of satisfactory consistent with the given data and be the simpler, less expressive model. Consistency is one of major measures of reliability in knowledge discovery. Therefore to develop an algorithm being able to derive a minimal model is an interesting topic in the are of reliable knowledge discovery. various causal induction algorithms and tools developed so far can not guarantee that the derived model is minimal and consistent. It was proved the MML induction approach introduced by Wallace, Keven and Honghua Dai is a minimal causal model learner. In this paper, we further prove that the developed minimal causal model learner is reliable in the sense of satisfactory consistency. The experimental results obtained from the tests on a number of both artificial and real models provided in this paper confirm this theoretical result.
ISBN 9780769549255
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
080110 Simulation and Modelling
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051780

Document type: Conference Paper
Collection: School of Information Technology
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