research-article

Direct local pattern sampling by efficient two-step random procedures

Authors:

Claudio Lucchese,

Thomas GärtnerAuthors Info & Claims

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 582 - 590

https://doi.org/10.1145/2020408.2020500

Published: 21 August 2011 Publication History

Abstract

We present several exact and highly scalable local pattern sampling algorithms. They can be used as an alternative to exhaustive local pattern discovery methods (e.g, frequent set mining or optimistic-estimator-based subgroup discovery) and can substantially improve efficiency as well as controllability of pattern discovery processes. While previous sampling approaches mainly rely on the Markov chain Monte Carlo method, our procedures are direct, i.e., non process-simulating, sampling algorithms. The advantages of these direct methods are an almost optimal time complexity per pattern as well as an exactly controlled distribution of the produced patterns. Namely, the proposed algorithms can sample (item-)sets according to frequency, area, squared frequency, and a class discriminativity measure. Experiments demonstrate that these procedures can improve the accuracy of pattern-based models similar to frequent sets and often also lead to substantial gains in terms of scalability.

References

[1]

R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In Advances in Knowl. Disc. and Data Min., pp. 307--328. 1996.

Digital Library

[2]

M. Al Hasan and M. J. Zaki. Output space sampling for graph patterns. PVLDB, 2(1):730--741, 2009.

Digital Library

[3]

S. D. Bay and M. J. Pazzani. Detecting group differences: Mining contrast sets. Data Min. Knowl. Discov., 5(3):213--246, 2001.

Digital Library

[4]

R. Bayardo, B. Goethals, and M. J. Zaki, editors. Proc. IEEE ICDM Workshop on Frequent Itemset Mining Implementations, 2004, vol. 126 of CEUR Workshop Proc.

[5]

S. Bistarelli and F. Bonchi. Soft constraint based pattern mining. Data and Knowl. Engineering, 62(1):118--137, 2007.

Digital Library

[6]

M. Boley, T. Gärtner, and H. Grosskreutz. Formal concept sampling for counting and threshold-free local pattern mining. In Proc. SIAM Int. Conf. on Data Mining (SDM 2010), pp. 177--188, 2010.

[7]

M. Boley and H. Grosskreutz. Approximating the number of frequent sets in dense data. Knowl. Inf. Syst., 21(1):65--89, 2009.

Digital Library

[8]

V. Chaoji, M. A. Hasan, S. Salem, J. Besson, and M. J. Zaki. Origami: A novel and effective approach for mining representative orthogonal graph patterns. Stat. Anal. Data Min., 1(2):67--84, 2008.

Digital Library

[9]

H. Cheng, X. Yan, J. Han, and C.-W. Hsu. Discriminative frequent pattern analysis for effective classification. In Proc. 23rd Int. Conf. on Data Engineering (ICDE 2007), pp. 716--725, 2007.

[10]

H. Cheng, X. Yan, J. Han, and P. S. Yu. Direct discriminative pattern mining for effective classification. In Proc. 24th Int. Conf. on Data Engineering (ICDE 2008), pp. 169--178, 2008.

Digital Library

[11]

J. Demsar. Statistical comparisons of classifiers over multiple data sets. J. of Mach. Learn. Res., 7:1--30, 2006.

Digital Library

[12]

G. Dong and J. Li. Efficient mining of emerging patterns: Discovering trends and differences. In Proc. 5th ACM SIGKDD Int. Conf. on Knowl. Disc. and Data Mining (KDD '99), pp. 43--52. ACM, 1999.

Digital Library

[13]

W. Fan, K. Zhang, H. Cheng, J. Gao, X. Yan, J. Han, P. S. Yu, and O. Verscheure. Direct mining of discriminative and essential frequent patterns via model-based search tree. In Proc. 14th Int. Conf. on Knowl. Disc. and Data Mining (KDD '08, pp. 230--238, 2008.

Digital Library

[14]

A. Frank and A. Asuncion. UCI machine learning repository, 2010.

[15]

F. Geerts, B. Goethals, and T. Mielikäinen. Tiling databases. In Proc. 7th Int. Disc. Science Conf., vol. 3245 of LNCS, pp. 278--289. Springer, 2004.

[16]

L. A. Goldberg. Efficient algorithms for listing combinatorial structures. Cambridge University Press, New York, NY, USA, 1993.

Digital Library

[17]

H. Grosskreutz, S. Rüping, and S. Wrobel. Tight optimistic estimates for fast subgroup discovery. In Proc. European Conf. on Machine Learning and Knowl. Disc. in Databases (ECML/PKDD 2008), Part I, vol. 5211 of LNCS, pp. 440--456, 2008.

Digital Library

[18]

D. Gunopulos, H. Mannila, and S. Saluja. Discovering all most specific sentences by randomized algorithms. In Proc. 6th Int. Conf. of Database Theory (ICDT '97), vol. 1186 of LNCS, pp. 215--229, 1997.

Digital Library

[19]

J. Han, J. Pei, Y. Yin, and R. Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov., 8(1):53--87, 2004.

Digital Library

[20]

D. J. Hand. Pattern detection and discovery. In Proc. ESF Workshop on Pattern Detection and Disc., vol. 2447 of LNCS, pp. 1--12. Springer, 2002.

Digital Library

[21]

R. M. Karp, M. Luby, and N. Madras. Monte-carlo approximation algorithms for enumeration problems. J. Algorithms, 10(3):429--448, 1989.

Digital Library

[22]

A. J. Knobbe, B. Crémilleux, J. Fürnkranz, and M. Scholz. From local patterns to global models: the lego approach to data mining. In From Local Patterns to Global Models: Proc. ECML/PKDD 2008 Workshop (LEGO '08), 2008.

[23]

S. Morishita and J. Sese. Traversing itemset lattice with statistical metric pruning. In Proc. 19th ACM SIGMOD-SIGACT-SIGART Symp. on Principles of Database Systems (PODS), pp. 226--236, 2000.

Digital Library

[24]

A. Pietracaprina and F. Vandin. Efficient incremental mining of top-k frequent closed itemsets. In Proc. 10th Int. Disc. Science Conf. (DS 2007), pp. 275--280, 2007.

Digital Library

[25]

T. Scheffer and S. Wrobel. Finding the most interesting patterns in a database quickly by using sequential sampling. J. of Mach. Learn. Res., 3:833--862, 2002.

Digital Library

[26]

T. Uno, T. Asai, Y. Uchida, and H. Arimura. An efficient algorithm for enumerating closed patterns in transaction databases. In Proc. 7th Int. Disc. Science Conf. (DS 2004), vol. 3245 of LNCS, pp. 16--31. Springer, 2004.

[27]

S. Wrobel. An algorithm for multi-relational discovery of subgroups. In Proc. 1st Euro. Symp. on Principles of Data Mining and Knowl. Disc. (PKDD '97), vol. 1263 of LNCS, pp. 78--87. Springer, 1997.

Digital Library

[28]

M. J. Zaki, S. Parthasarathy, W. Li, and M. Ogihara. Evaluation of sampling for data mining of association rules. In Proc. 7th Workshop on Research Issues in Data Engineering (RIDE), 1997.

Digital Library

Cited By

Ciaperoni MXiao HGionis A(2023)Concise and interpretable multi-label rule setsKnowledge and Information Systems10.1007/s10115-023-01930-665:12(5657-5694)Online publication date: 28-Jul-2023
https://doi.org/10.1007/s10115-023-01930-6
Panahbehagh BJauslin RTillé Y(2023)A general stream sampling designComputational Statistics10.1007/s00180-023-01408-7Online publication date: 20-Sep-2023
https://doi.org/10.1007/s00180-023-01408-7
Tang HWang LLiu YQian J(2022)Discovering Approximate and Significant High‐Utility Patterns from Transactional DatasetsJournal of Mathematics10.1155/2022/69751302022:1Online publication date: 16-Nov-2022
https://doi.org/10.1155/2022/6975130
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Index Terms

Direct local pattern sampling by efficient two-step random procedures
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2011

1446 pages

ISBN:9781450308137

DOI:10.1145/2020408

General Chair:
Chid Apte
IBM Research
,
Program Chairs:
Joydeep Ghosh
UT Austin
,
Padhraic Smyth
UC Irvine

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 August 2011

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KDD '11: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2011

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Ciaperoni MXiao HGionis A(2023)Concise and interpretable multi-label rule setsKnowledge and Information Systems10.1007/s10115-023-01930-665:12(5657-5694)Online publication date: 28-Jul-2023
https://doi.org/10.1007/s10115-023-01930-6
Panahbehagh BJauslin RTillé Y(2023)A general stream sampling designComputational Statistics10.1007/s00180-023-01408-7Online publication date: 20-Sep-2023
https://doi.org/10.1007/s00180-023-01408-7
Tang HWang LLiu YQian J(2022)Discovering Approximate and Significant High‐Utility Patterns from Transactional DatasetsJournal of Mathematics10.1155/2022/69751302022:1Online publication date: 16-Nov-2022
https://doi.org/10.1155/2022/6975130
Pellegrina LCousins CVandin FRiondato M(2022)MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern MiningACM Transactions on Knowledge Discovery from Data10.1145/353218716:6(1-29)Online publication date: 30-Jul-2022
https://dl.acm.org/doi/10.1145/3532187
Ciaperoni MXiao HGionis A(2022)Concise and interpretable multi-label rule sets2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00017(71-80)Online publication date: Nov-2022
https://doi.org/10.1109/ICDM54844.2022.00017
Diop LDiop CGiacometti ASoulet A(2022)Trie-based Output Space Itemset Sampling2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020843(6-15)Online publication date: 17-Dec-2022
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Diop LDiop CGiacometti ASoulet A(2022)Pattern on demand in transactional distributed databasesInformation Systems10.1016/j.is.2021.101908104:COnline publication date: 1-Feb-2022
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Proença HGrünwald PBäck Tvan Leeuwen M(2022)Robust subgroup discoveryData Mining and Knowledge Discovery10.1007/s10618-022-00856-x36:5(1885-1970)Online publication date: 12-Aug-2022
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Galbrun E(2022)The minimum description length principle for pattern mining: a surveyData Mining and Knowledge Discovery10.1007/s10618-022-00846-z36:5(1679-1727)Online publication date: 4-Jul-2022
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Diop L(2022)High Average-Utility Itemset Sampling Under Length ConstraintsAdvances in Knowledge Discovery and Data Mining10.1007/978-3-031-05936-0_11(134-148)Online publication date: 16-May-2022
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