TY - JFULL AU - Luminita Dumitriu and Cristina Segal and Marian Craciun and Adina Cocu and Lucian P. Georgescu PY - 2007/12/ TI - Model Discovery and Validation for the Qsar Problem using Association Rule Mining T2 - International Journal of Mathematical and Computational Sciences SP - 545 EP - 550 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/13096 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 11, 2007 N2 - 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. ER -