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Abstract: Subgroup discovery aims at finding interesting subsets of a classified example set that deviates from the overall distribution. The search is guided by a so-called utility function, trading the size of subsets (coverage) against their statistical unusualness. By choosing the utility function accordingly, subgroup discovery is well suited to find interesting rules with much smaller coverage and bias than possible with standard classifier induction algorithms. Smaller subsets can be... (Update)
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1.4: Knowledge-Based Sampling for Subgroup Discovery - Scholz (2005) (Correct)
0.9: Sampling-Based Sequential Subgroup Mining - Martin Scholz Artificial (Correct)
0.4: On the Complexity of Rule Discovery from Distributed Data - Scholz (2005) (Correct)
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BibTeX entry: (Update)
@misc{ scholz-knowledgebased,
author = "Discovery Martin Scholz",
title = "Knowledge-Based Sampling for Subgroup",
url = "citeseer.ist.psu.edu/765713.html" }
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