Title :
LEFRA: learning from associations
Author :
Hashemi, R.R. ; LeBlanc, L. ; Westgeest, D.J. ; Tyler, A.A.
Author_Institution :
Department of Computer Science, Armstrong Atlantic State University, Savannah, CA 31419
fDate :
June 28 2004-July 1 2004
Abstract :
In data mining, a Multi-level Association Analysis (MAA) produces a set of association rules. These rules mainly identify those values of multiple attributes that are associated to cach other. In this paper, we introduce a new learning paradigm based on association rules called ??Learning from Association (LEFRA)?? which is used as a part of 8 predictive system to predict the effect of a number of carcinogens on liver. The validity of the proposed learning paradigm is established by comparing its performance with the performance of logistic regression which has been applied on the same dataset .
Keywords :
Algorithm design and analysis; Computer science; Data mining; Educational institutions; Logistics; Optical wavelength conversion; Association Analysis; Data Mining; Learning from Association; Predictive Systems;
Conference_Titel :
Automation Congress, 2004. Proceedings. World
Conference_Location :
Seville
Print_ISBN :
1-889335-21-5
