skip to main content
10.1145/2020408.2020500acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Direct local pattern sampling by efficient two-step random procedures

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.
[2]
M. Al Hasan and M. J. Zaki. Output space sampling for graph patterns. PVLDB, 2(1):730--741, 2009.
[3]
S. D. Bay and M. J. Pazzani. Detecting group differences: Mining contrast sets. Data Min. Knowl. Discov., 5(3):213--246, 2001.
[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.
[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.
[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.
[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.
[11]
J. Demsar. Statistical comparisons of classifiers over multiple data sets. J. of Mach. Learn. Res., 7:1--30, 2006.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[21]
R. M. Karp, M. Luby, and N. Madras. Monte-carlo approximation algorithms for enumeration problems. J. Algorithms, 10(3):429--448, 1989.
[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.
[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.
[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.
[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.
[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.

Cited By

View all
  • (2025) Rules Describing CO 2 Activation on Single-Atom Alloys from DFT-Meta-GGA Calculations and Artificial Intelligence ACS Catalysis10.1021/acscatal.4c0717815:4(2916-2926)Online publication date: 4-Feb-2025
  • (2024)Scalable Sampling for High Utility Patterns2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10826031(104-109)Online publication date: 15-Dec-2024
  • (2024)RPS: A Generic Reservoir Patterns Sampler2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825601(110-115)Online publication date: 15-Dec-2024
  • Show More Cited By

Index Terms

  1. Direct local pattern sampling by efficient two-step random procedures

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    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
    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 August 2011

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. frequent sets
    2. local pattern discovery
    3. pattern-based classification
    4. sampling

    Qualifiers

    • Research-article

    Conference

    KDD '11
    Sponsor:

    Acceptance Rates

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

    Upcoming Conference

    KDD '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)20
    • Downloads (Last 6 weeks)5
    Reflects downloads up to 25 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2025) Rules Describing CO 2 Activation on Single-Atom Alloys from DFT-Meta-GGA Calculations and Artificial Intelligence ACS Catalysis10.1021/acscatal.4c0717815:4(2916-2926)Online publication date: 4-Feb-2025
    • (2024)Scalable Sampling for High Utility Patterns2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10826031(104-109)Online publication date: 15-Dec-2024
    • (2024)RPS: A Generic Reservoir Patterns Sampler2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825601(110-115)Online publication date: 15-Dec-2024
    • (2024)Data Aestheticization: A Cognitively-Inspired Method for Knowledge Discovery in Cognitive IoT Sensor NetworkWireless Personal Communications: An International Journal10.1007/s11277-024-11653-8139:2(1039-1070)Online publication date: 1-Nov-2024
    • (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
    • (2023)A general stream sampling designComputational Statistics10.1007/s00180-023-01408-739:6(2899-2924)Online publication date: 20-Sep-2023
    • (2022)Discovering Approximate and Significant High‐Utility Patterns from Transactional DatasetsJournal of Mathematics10.1155/2022/69751302022:1Online publication date: 16-Nov-2022
    • (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
    • (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
    • (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
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media