Abstract
Knowledge Discovery in Databases (KDD) is a data analysis process which, in contrast to conventional data analysis, automatically generates and evaluates very many hypotheses, deals with complex, i.e. large, high dimensional, multi relational, dynamic, or heterogeneous data, and produces understandable results for those who “own the data”. With these objectives, subgroup mining searches for hypotheses that can be supported or confirmed by the given data and that are represented as a specialization of one of three general hypothesis types: deviating subgroups, associations between two subgroups, and partially ordered sets of subgroups where the partial ordering usually relates to time. This paper gives a short introduction into the methods of subgroup mining. Especially the main preprocessing, data mining and postprocessing steps are discussed in more detail for two applications. We conclude with some problems of the current state of the art of subgroup mining.
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Klösgen, W. (1999). Applications and research problems of subgroup mining. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095086
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DOI: https://doi.org/10.1007/BFb0095086
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