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An association rule analysis framework for complex physiological and genetic data

He, Jing, Zhang, Yanchun, Huang, Guangyan, Xin, Yefei, Liu, Xiaohui, Zhang, Hao Lan, Chiang, Stanley and Zhang, Hailun 2012, An association rule analysis framework for complex physiological and genetic data, in First International Conference, HIS 2012, Beijing, China, April 8-10, 2012. Proceedings, Springer, Berlin, Germany, pp. 131-142, doi: 10.1007/978-3-642-29361-0_17.

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Title An association rule analysis framework for complex physiological and genetic data
Author(s) He, Jing
Zhang, Yanchun
Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
Xin, Yefei
Liu, Xiaohui
Zhang, Hao Lan
Chiang, Stanley
Zhang, Hailun
Conference name International Conference on Health Information Science (1st : 2012 : Beijing, China)
Conference location Beijing, China
Conference dates 8-10 Apr. 2012
Title of proceedings First International Conference, HIS 2012, Beijing, China, April 8-10, 2012. Proceedings
Editor(s) He,J
Liu,X
Krupinski,EA
Xu,G
Publication date 2012
Series Lecture Notes in Computer Science v.7231
Conference series Health Information Science
Start page 131
End page 142
Total pages 12
Publisher Springer
Place of publication Berlin, Germany
Summary Physiological and genetic information has been critical to the successful diagnosis and prognosis of complex diseases. In this paper, we introduce a support-confidence-correlation framework to accurately discover truly meaningful and interesting association rules between complex physiological and genetic data for disease factor analysis, such as type II diabetes (T2DM). We propose a novel Multivariate and Multidimensional Association Rule mining system based on Change Detection (MMARCD). Given a complex data set u i (e.g. u 1 numerical data streams, u 2 images, u 3 videos, u 4 DNA/RNA sequences) observed at each time tick t, MMARCD incrementally finds correlations and hidden variables that summarise the key relationships across the entire system. Based upon MMARCD, we are able to construct a correlation network for human diseases. © 2012 Springer-Verlag.
ISBN 9783642293610
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-642-29361-0_17
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2012, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083688

Document type: Conference Paper
Collection: School of Information Technology
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