File(s) under permanent embargo
Optimized fuzzy association rule mining for quantitative data
conference contribution
posted on 2014-01-01, 00:00 authored by H Zheng, J He, Guangyan HuangGuangyan Huang, Y ZhangWith 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.
History
Event
IEEE International Conference on Fuzzy Systems (2014 : Beijing, China)Pagination
396 - 403Publisher
IEEELocation
Beijing, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-07-06End date
2014-07-11ISSN
1098-7584ISBN-13
9781479920723Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2014, IEEETitle of proceedings
FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy SystemsUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC