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.

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https://doi.org/10.1021/acscatal.4c07178
Diop LPlantevit M(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
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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

California, San Diego, USA

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

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https://doi.org/10.1021/acscatal.4c07178
Diop LPlantevit M(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
https://doi.org/10.1109/BigData62323.2024.10826031
Diop LPlantevit MSoulet A(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
https://doi.org/10.1109/BigData62323.2024.10825601
Jha VTripathi P(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
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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
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Panahbehagh BJauslin RTillé Y(2023)A general stream sampling designComputational Statistics10.1007/s00180-023-01408-739:6(2899-2924)Online publication date: 20-Sep-2023
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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
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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
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