2009 CorrelatedItemsetMininginROCSpa

• (Nijssen et al., 2009) ⇒ Siegfried Nijssen, Tias Guns, and Luc De Raedt. (2009). “Correlated Itemset Mining in ROC Space: A Constraint Programming Approach.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557092

Subject Headings:

Notes

• Categories and Subject Descriptors: H.2.8 Database Management: Database applications — Data Mining; F.4.1 Mathematical Logic and Formal Languages: Mathematical Logic — Logic and Constraint Programming.
• General Terms: Algorithms, Theory

Cited By

• http://scholar.google.com/scholar?q=%22Correlated+itemset+mining+in+ROC+space%3A+a+constraint+programming+approach%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557092&preflayout=flat#citedby

Quotes

Author Keywords

Itemset Mining, Constraint Programming, ROC Analysis

Abstract

Correlated or discriminative pattern mining is concerned with finding the highest scoring patterns w.r.t. a correlation measure (such as information gain). By reinterpreting correlation measures in ROC space and formulating correlated itemset mining as a constraint programming problem, we obtain new theoretical insights with practical benefits. More specifically, we contribute 1) an improved bound for correlated itemset miners, 2) a novel iterative pruning algorithm to exploit the bound, and 3) an adaptation of this algorithm to mine all itemsets on the convex hull in ROC space. The algorithm does not depend on a minimal frequency threshold and is shown to outperform several alternative approaches by orders of magnitude, both in runtime and in memory requirements.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 CorrelatedItemsetMininginROCSpa	Luc De Raedt Siegfried Nijssen Tias Guns			Correlated Itemset Mining in ROC Space: A Constraint Programming Approach		KDD-2009 Proceedings		10.1145/1557019.1557092		2009