A Graph-based Approach for Comparing Interestingness Measures

Author

Huynh, Xuan-Hiep ; Guillet, Fabrice ; Briand, Henri

Author_Institution

Polytech. Sch., Nantes Univ.

fYear

0

fDate

0-0 0

Firstpage

1

Lastpage

6

Abstract

In the context of data mining, we use the Spearman´s rank correlation coefficient in order to compare the behavior of 40 interestingness measures of association rules. Via a new graph-based approach, we can visualize not only the strong but also the weak correlations between interestingness measures. We propose to discover the stable clusters of interestingness measures (i.e. subsets of interestingness measures delivering a close rule ranking) by making comparative study on two opposite datasets (a highly correlated one and a lowly correlated one). The results show that the correlation between interestingness measures depends on data nature and rule ranks, and show also 6 stable clusters

Keywords

correlation methods; data mining; graph theory; Spearman rank correlation coefficient; association rules; data mining; graph-based approach; interestingness measures; Association rules; Costs; Data mining; Data structures; Data visualization; Educational institutions; Intensity modulation; Itemsets; Prototypes; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering of Intelligent Systems, 2006 IEEE International Conference on

Conference_Location

Islamabad

Print_ISBN

1-4244-0456-8

Type

conf

DOI

10.1109/ICEIS.2006.1703196

Filename

1703196