文章基本信息

标题：Descriptive mining for the QSAR problem
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作者：Luminita DUMITRIU ; Marian CRACIUN ; Cristina SEGAL 等
期刊名称：Annals of “Dunarea de Jos”
印刷版ISSN：1221-454X
出版年度：2005
卷号：2005
出版社：“Dunarea de Jos” University of Galati
摘要：There are several approaches in trying to solve the Quantitative Structure-Activity (QSAR) problem. These approaches are based either on statistical methods or on predictive data mining using neural networks. 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. These approaches have a low explanatory capability or non at all. This paper attempts to establish a new approach in solving QSSAR problems using descriptive data mining. This way, the relationship between the chemical properties and the activity of a substance would be comprehensibly modeled
关键词：Quantitative Structure-Activity Relationship, data mining, association rules, classification.