Title :
Mining Negative and Positive Influence Rules Using Kullback-Leibler Divergence
Author :
Alachaher, Leila Nemmiche ; Guillaume, Sylvie
Author_Institution :
Lab. LIMOS, Univ. Blaise Pascal, Aubiere
Abstract :
This paper describes a new method for mining negative and positive quantitative influence rules based on a coordination between a statistical dissimilarity measure (Kullback Leibler divergence) and contingency tables. This coordination identifies the significant positive and negative correlations and enables pertinent influence rules extraction.
Keywords :
data mining; Kullback-Leibler divergence; influence rules extraction; negative quantitative influence rule mining; positive quantitative influence rule mining; Association rules; Data mining; Decision making; Itemsets; Stress; Transaction databases;
Conference_Titel :
Computing in the Global Information Technology, 2007. ICCGI 2007. International Multi-Conference on
Conference_Location :
Guadeloupe City
Print_ISBN :
0-7695-2798-1
DOI :
10.1109/ICCGI.2007.38
