%0 Journal Article
	%A Luminita Dumitriu and  Cristina Segal and  Marian Craciun and  Adina Cocu and  Lucian P. Georgescu
	%D 2007
	%J International Journal of Mathematical and Computational Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 11, 2007
	%T Model Discovery and Validation for the Qsar Problem using Association Rule Mining
	%U https://publications.waset.org/pdf/13096
	%V 11
	%X There are several approaches in trying to solve the
Quantitative 1Structure-Activity Relationship (QSAR) problem.
These approaches are based either on statistical methods or on
predictive data mining. Among the statistical methods, one should
consider regression analysis, pattern recognition (such as cluster
analysis, factor analysis and principal components analysis) or partial
least squares. Predictive data mining techniques use either neural
networks, or genetic programming, or neuro-fuzzy knowledge. These
approaches have a low explanatory capability or non at all. This
paper attempts to establish a new approach in solving QSAR
problems using descriptive data mining. This way, the relationship
between the chemical properties and the activity of a substance
would be comprehensibly modeled.
	%P 546 - 550