2012 AnEnhancedRelevanceCriterionfor

• (Großkreutz et al., 2012) ⇒ Henrik Großkreutz, Daniel Paurat, and Stefan Rüping. (2012). “An Enhanced Relevance Criterion for More Concise Supervised Pattern Discovery.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339756

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+An+Enhanced+Relevance+Criterion+for+More+Concise+Supervised+Pattern+Discovery
• http://dl.acm.org/citation.cfm?id=2339530.2339756&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; local patterns; subgroup discovery; theory of relevance

Abstract

Supervised local pattern discovery aims to find subsets of a database with a high statistical unusualness in the distribution of a target attribute. Local pattern discovery is often used to generate a human-understandable representation of the most interesting dependencies in a data set. Hence, the more crisp and concise the output is, the better. Unfortunately, standard algorithm often produce very large and redundant outputs.

In this paper, we introduce delta-relevance, a definition of a more strict criterion of relevance. It will allow us to significantly reduce the output space, while being able to guarantee that every local pattern has a delta-relevant representative which is almost as good in a clearly defined sense. We show empirically that delta-relevance leads to a considerable reduction of the amount of returned patterns. We also demonstrate that in a top-k setting, the removal of not delta-relevant patterns improves the quality of the result set.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 AnEnhancedRelevanceCriterionfor	Daniel Paurat Henrik Großkreutz Stefan Rüping			An Enhanced Relevance Criterion for More Concise Supervised Pattern Discovery				10.1145/2339530.2339756		2012