Abstract
We describe an algorithm called TargetCluster for the discretization of continuous targets in subgroup discovery. The algorithm identifies patterns in the target data and uses them to select the discretization cutpoints. The algorithm has been implemented in a subgroup discovery method. Tests show that the discretization method likely leads to improved insight.
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Moreland, K., Truemper, K. (2009). Discretization of Target Attributes for Subgroup Discovery. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_4
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DOI: https://doi.org/10.1007/978-3-642-03070-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03069-7
Online ISBN: 978-3-642-03070-3
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