Model Discovery and Validation for the Qsar Problem using Association Rule Mining

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@Article{Dumitriu:2007:ISI,

• author = "Luminita Dumitriu and Cristina Segal and Marian Craciun and Adina Cocu and Lucian P. Georgescu",
• title = "Model Discovery and Validation for the Qsar Problem using Association Rule Mining",
• journal = "International Science Index",
• volume = "1",
• number = "11",
• year = "2007",
• pages = "648--652",
• keywords = "genetic algorithms, genetic programming, association rules, classification, data mining, quantitative structure - activity relationship.",
• bibsource = "http://waset.org/Publications",
• publisher = "World Academy of Science, Engineering and Technology",
• ISSN = "1307-6892",
• oai = "oai:CiteSeerX.psu:10.1.1.308.3241",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.3241",
• URL = "http://www.waset.org/journals/waset/v11/v11-129.pdf",
• URL = "http://waset.org/publications/13096",
• URL = "http://waset.org/Publications?p=11",
• size = "5 pages",
• abstract = "There are several approaches in trying to solve the Quantitative Structure-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 modelled.",
• notes = "International Science Index 11, 2007",

}

Genetic Programming entries for Luminita Dumitriu Cristina Segal Marian Craciun Adina Cocu Lucian P Georgescu

Citations