Optimized fuzzy association rule mining for quantitative data

Zheng, Hui, He, Jing, Huang, Guangyan and Zhang, Yanchun 2014, Optimized fuzzy association rule mining for quantitative data, in FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 396-403, doi: 10.1109/FUZZ-IEEE.2014.6891735.

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Title Optimized fuzzy association rule mining for quantitative data
Author(s) Zheng, Hui
He, Jing
Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
Zhang, Yanchun
Conference name IEEE International Conference on Fuzzy Systems (2014 : Beijing, China)
Conference location Beijing, China
Conference dates 6-11 Jul. 2014
Title of proceedings FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems
Publication date 2014
Start page 396
End page 403
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) quantitative
association rule
fuzzy sets
optimized partition points
objective function
Summary With the advance of computing and electronic technology, quantitative data, for example, continuous data (i.e., sequences of floating point numbers), become vital and have wide applications, such as for analysis of sensor data streams and financial data streams. However, existing association rule mining generally discover association rules from discrete variables, such as boolean data (`O' and `l') and categorical data (`sunny', `cloudy', `rainy', etc.) but very few deal with quantitative data. In this paper, a novel optimized fuzzy association rule mining (OFARM) method is proposed to mine association rules from quantitative data. The advantages of the proposed algorithm are in three folds: 1) propose a novel method to add the smoothness and flexibility of membership function for fuzzy sets; 2) optimize the fuzzy sets and their partition points with multiple objective functions after categorizing the quantitative data; and 3) design a two-level iteration to filter frequent-item-sets and fuzzy association-rules. The new method is verified by three different data sets, and the results have demonstrated the effectiveness and potentials of the developed scheme.
ISBN 9781479920723
ISSN 1098-7584
Language eng
DOI 10.1109/FUZZ-IEEE.2014.6891735
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
Grant ID LP100200682
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079916

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