Mining rank-correlated sets of numerical attributes

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Mining rank-correlated sets of numerical attributes

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Philadelphia, PA, USA

SESSION: Research track papers table of contents

Pages: 96 - 105

Year of Publication: 2006

ISBN:1-59593-339-5

Authors

Toon Calders	University of Antwerp
Bart Goethals	University of Antwerp
Szymon Jaroszewicz	Szczecin University of Technology, Warsaw, Poland

Sponsors

ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 10, Downloads (12 Months): 70, Citation Count: 2

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ABSTRACT

We study the mining of interesting patterns in the presence of numerical attributes. Instead of the usual discretization methods, we propose the use of rank based measures to score the similarity of sets of numerical attributes. New support measures for numerical data are introduced, based on extensions of Kendall's tau, and Spearman's Footrule and rho. We show how these support measures are related. Furthermore, we introduce a novel type of pattern combining numerical and categorical attributes. We give efficient algorithms to find all frequent patterns for the proposed support measures, and evaluate their performance on real-life datasets.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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	Christoph F. Eick , Rachana Parmar , Wei Ding , Tomasz F. Stepinski , Jean-Philippe Nicot, Finding regional co-location patterns for sets of continuous variables in spatial datasets, Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, November 05-07, 2008, Irvine, California
	Gaurav Pandey , Gowtham Atluri , Michael Steinbach , Chad L. Myers , Vipin Kumar, An association analysis approach to biclustering, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, June 28-July 01, 2009, Paris, France