Using Genetic Algorithms to Mine Interesting Dependence Modeling Rules

A. Simões Gonçalves (Brazil), A. Alves Freitas (UK), R. Kato, and R.C. Limão de Oliveira (Brazil)


Data Mining, Dependence Modeling, Genetic Algorithms, Knowledge Discovery in Databases


The challenge of KDD (Knowledge Discovery in Databases) is to efficiently and automatically analyze the available information, extracting useful knowledge from databases. We present GenMiner - a Data Mining tool for the Dependence Modeling task. GenMiner is a genetic algorithm based tool that searches for interesting rules involving correlated attributes on a relational database. Generated rules are evaluated on an individual basis, favoring accurate and surprising rules. The genetic individual encoding provided relational database integration. Chromosomes in our GA are represented by SQL queries. GenMiner evaluation was based on a public domain database including information about applications for nursery schools.

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