Abstract
Most valid rules that are learned from very large and high dimensional data sets are not interesting, but are already known to the users. The dominant model of the overall data set may well suppress the interesting local patterns. The search for interesting local patterns can be implemented by a two step learning approach which first acquires the global models before it focuses on the rest in order to detect local patterns. In this paper, three sets of interesting instances are distinguished. For these sets, the hypothesis space is enlarged in order to characterize local patterns in a second learning step.
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Morik, K. (2002). Detecting Interesting Instances. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds) Pattern Detection and Discovery. Lecture Notes in Computer Science(), vol 2447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45728-3_2
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DOI: https://doi.org/10.1007/3-540-45728-3_2
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