Abstract
We describe an approach and a tool for the discovery of subgroups within the framework of distribution rule mining. Distribution rules are a kind of association rules particularly suited for the exploratory study of numerical variables of interest. Being an exploratory technique, the result of a distribution mining process is typically a very large number of patterns. Exploring such results is thus a complex task and limits the use of the technique. To overcome this shortcoming we developed a tool, written in Java, which supports subgroup discovery in a post-processing step. The tool engages the analyst in an interactive process of subgroup discovery by means of a graphical interface with well defined statistical grounds, where domain knowledge can be used during the identification of such subgroups amid the population. We show a case study to analyze the results of students in a large scale university admission examination.
Supported by POCI/TRA/61001/2004 Triana Project (Fundação Ciência e Tecnologia), FEDER e Programa de Financiamento Plurianual de Unidades de I & D.
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Lucas, J.P., Jorge, A.M., Pereira, F., Pernas, A.M., Machado, A.A. (2007). A Tool for Interactive Subgroup Discovery Using Distribution Rules. In: Neves, J., Santos, M.F., Machado, J.M. (eds) Progress in Artificial Intelligence. EPIA 2007. Lecture Notes in Computer Science(), vol 4874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77002-2_36
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DOI: https://doi.org/10.1007/978-3-540-77002-2_36
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