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): 8, Downloads (12 Months): 58, 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.

	1	Rakesh Agrawal , Tomasz Imieliński , Arun Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., United States
	2	Yonatan Aumann , Yehuda Lindell, A statistical theory for quantitative association rules, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.261-270, August 15-18, 1999, San Diego, California, United States [doi>10.1145/312129.312243]
	3	Jon Louis Bentley , Jerome H. Friedman, Data Structures for Range Searching, ACM Computing Surveys (CSUR), v.11 n.4, p.397-409, Dec. 1979 [doi>10.1145/356789.356797]
	4	F. Bodon and L. Schmidt-Thieme. The relation of closed itemset mining, complete pruning strategies and item ordering in apriori-based fim algorithms. In Proc. PKDD, pages 437--444, 2005.
	5	T. Calders and B. Goethals. Depth-first non-derivable itemset mining. In Proc. SIAM DM, 2005.
	6	P. Diaconis and R. Graham. Spearman's footrule as a measure of disarray. J. of the Royal Statistical Society, Series B, 39(2):262--268, 1977.
	7	S. Dzeroski and L. Todorovski. Discovering dynamics: From inductive logic programming to machine discovery. Journal of Intelligent Information Systems, 4:89--108, 1995.
	8	Peter W. Frey , David J. Slate, Letter Recognition Using Holland-Style Adaptive Classifiers, Machine Learning, v.6 n.2, p.161-182, March 1991 [doi>10.1023/A:1022606404104]
	9	B. Goethals. Frequent set mining. In In The Data Mining and Knowledge Discovery Handbook, chapter 17, pages 377--397. 2005.
	10	S. Hettich and S. D. Bay. The UCI KDD Archive {http://kdd.ics.uci.edu} Irvine, CA: University of California, Dept. of inf. and comp. science, 1999.
	11	Szymon Jaroszewicz , Tobias Scheffer, Fast discovery of unexpected patterns in data, relative to a Bayesian network, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA [doi>10.1145/1081870.1081887]
	12	M. Kendall. Rank Correlation Methods. Oxford University Press, 1990.
	13	A. M. Liebetrau. Measures of Association, volume 32 of Quantitative Applications in the Social Sciences. Sage Publications, Inc., 1983.
	14	Hongjun Lu , Ling Feng , Jiawei Han, Beyond intratransaction association analysis: mining multidimensional intertransaction association rules, ACM Transactions on Information Systems (TOIS), v.18 n.4, p.423-454, Oct. 2000 [doi>10.1145/358108.358114]
	15	G. I. Webb S. Huang. Discarding insignificant rules during impact rule discovery in large databases. In Proc. SIAM DM, pages 541--545, 2005.
	16	Ramakrishnan Srikant , Rakesh Agrawal, Mining quantitative association rules in large relational tables, Proceedings of the 1996 ACM SIGMOD international conference on Management of data, p.1-12, June 04-06, 1996, Montreal, Quebec, Canada
	17	Michael Steinbach , Pang-Ning Tan , Hui Xiong , Vipin Kumar, Generalizing the notion of support, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014141]

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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