Abstract
This chapter investigates subgroup discovery as a task of constraint-based mining of local patterns, aimed at describing groups of individuals with unusual distributional characteristics with respect to the property of interest. The chapter provides a novel interpretation of relevancy constraints and their use for feature filtering, introduces relevancy-based mechanisms for handling unknown values in the examples, and discusses the concept of relevancy as an approach to avoiding overfitting in subgroup discovery. The proposed approach to constraint-based subgroup mining, using the SD algorithm, was successfully applied to gene expression data analysis in functional genomics.
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Lavrač, N., Gamberger, D. (2006). Relevancy in Constraint-Based Subgroup Discovery. In: Boulicaut, JF., De Raedt, L., Mannila, H. (eds) Constraint-Based Mining and Inductive Databases. Lecture Notes in Computer Science(), vol 3848. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11615576_12
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DOI: https://doi.org/10.1007/11615576_12
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