DocumentCode
3451994
Title
Quantitative Association Rules Mining Method Based on Trapezium Cloud Model
Author
Zhao-hong Wang
Author_Institution
Dept. of Comput. Sci. & Technol., Weifang Univ., Weifang, China
fYear
2010
fDate
27-28 Nov. 2010
Firstpage
1
Lastpage
4
Abstract
The quantitative association rules mining method is difficult for their values are too large. The usual means is dividing quantitative Data to discrete conception. The trapezium Cloud model combines ambiguity and randomness organically to fit the real world objectively, divide quantitative Data with trapezium Cloud model to create concepts, the concept cluster within one class, and separated with each other. So the quantitative Data can be transforms to Boolean data well, the Boolean data can be mined by the mature Boolean association rules mining method.
Keywords
Boolean functions; cloud computing; data mining; pattern clustering; set theory; Boolean data; conception division; quantitative association rules mining; trapezium cloud model; Association rules; Clouds; Data models; Distribution functions; Entropy; Mathematical model;
fLanguage
English
Publisher
ieee
Conference_Titel
Database Technology and Applications (DBTA), 2010 2nd International Workshop on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-6975-8
Electronic_ISBN
978-1-4244-6977-2
Type
conf
DOI
10.1109/DBTA.2010.5658963
Filename
5658963
Link To Document