research-article

Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms

Authors:

P. Krishna ReddyAuthors Info & Claims

EDBT/ICDT '11: Proceedings of the 14th International Conference on Extending Database Technology

Pages 11 - 20

https://doi.org/10.1145/1951365.1951370

Published: 21 March 2011 Publication History

Abstract

Frequent patterns are an important class of regularities that exist in a transaction database. Certain frequent patterns with low minimum support (minsup) value can provide useful information in many real-world applications. However, extraction of these frequent patterns with single minsup-based frequent pattern mining algorithms such as Apriori and FP-growth leads to "rare item problem." That is, at high minsup value, the frequent patterns with low minsup are missed, and at low minsup value, the number of frequent patterns explodes. In the literature, "multiple minsups framework" was proposed to discover frequent patterns. Furthermore, frequent pattern mining techniques such as Multiple Support Apriori and Conditional Frequent Pattern-growth (CFP-growth) algorithms have been proposed. As the frequent patterns mined with this framework do not satisfy downward closure property, the algorithms follow different types of pruning techniques to reduce the search space. In this paper, we propose an efficient CFP-growth algorithm by proposing new pruning techniques. Experimental results show that the proposed pruning techniques are effective.

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Cited By

Huynh BTung NNguyen TTrinh CSnasel VNguyen L(2024)New approaches for mining high utility itemsets with multiple utility thresholdsApplied Intelligence10.1007/s10489-023-05145-854:1(767-790)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05145-8
Saraiva GApolinário FPardal M(2024)IM-DISCO: Invariant Mining for Detecting IntrusionS in Critical OperationsComputer Security. ESORICS 2023 International Workshops10.1007/978-3-031-54129-2_3(42-58)Online publication date: 12-Mar-2024
https://doi.org/10.1007/978-3-031-54129-2_3
Tian WLi FLiu YWang ZZhang T(2023)Depth-First Uncertain Frequent Itemsets Mining based on Ensembled Conditional Item-Wise Supports2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)10.1109/ISBP57705.2023.10061307(121-128)Online publication date: 6-Jan-2023
https://doi.org/10.1109/ISBP57705.2023.10061307
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Index Terms

Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Other conferences

EDBT/ICDT '11: Proceedings of the 14th International Conference on Extending Database Technology

March 2011

587 pages

ISBN:9781450305280

DOI:10.1145/1951365

Editors:
Anastasia Ailamaki
EPFL, Switzerland
,
Sihem Amer-Yahia
Yahoo! Research
,
Jignesh Pate
University of Wisconsin-Madison
,
Tore Risch
Uppsala University, Sweden
,
Pierre Senellart
Télécom ParisTech, France
,
Julia Stoyanovich
University of Pennsylvania

Copyright © 2011 ACM.

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New York, NY, United States

Publication History

Published: 21 March 2011

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EDBT/ICDT '11

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Microsoft Research

EDBT/ICDT '11: EDBT/ICDT '11 joint conference

March 21 - 24, 2011

Uppsala, Sweden

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Overall Acceptance Rate 7 of 10 submissions, 70%

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Cited By

Huynh BTung NNguyen TTrinh CSnasel VNguyen L(2024)New approaches for mining high utility itemsets with multiple utility thresholdsApplied Intelligence10.1007/s10489-023-05145-854:1(767-790)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05145-8
Saraiva GApolinário FPardal M(2024)IM-DISCO: Invariant Mining for Detecting IntrusionS in Critical OperationsComputer Security. ESORICS 2023 International Workshops10.1007/978-3-031-54129-2_3(42-58)Online publication date: 12-Mar-2024
https://doi.org/10.1007/978-3-031-54129-2_3
Tian WLi FLiu YWang ZZhang T(2023)Depth-First Uncertain Frequent Itemsets Mining based on Ensembled Conditional Item-Wise Supports2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)10.1109/ISBP57705.2023.10061307(121-128)Online publication date: 6-Jan-2023
https://doi.org/10.1109/ISBP57705.2023.10061307
Erba ATippenhauer N(2022)Assessing Model-free Anomaly Detection in Industrial Control Systems Against Generic Concealment AttacksProceedings of the 38th Annual Computer Security Applications Conference10.1145/3564625.3564633(412-426)Online publication date: 5-Dec-2022
https://dl.acm.org/doi/10.1145/3564625.3564633
Alhusaini NLi JFournier-Viger PHawbani AChen G(2022)Mining High Utility Itemset with Multiple Minimum Utility Thresholds Based on Utility Deviation2022 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW58026.2022.00071(490-496)Online publication date: Nov-2022
https://doi.org/10.1109/ICDMW58026.2022.00071
Singh KKumar RBiswas B(2022)High average-utility itemsets mining: a surveyApplied Intelligence10.1007/s10489-021-02611-z52:4(3901-3938)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10489-021-02611-z
Fakir YMaarouf AEl Ayachi R(2022)Mining Frequents Itemset and Association Rules in Diabetic DatasetBusiness Intelligence10.1007/978-3-031-06458-6_12(146-157)Online publication date: 13-May-2022
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Darrab SBroneske DSaake G(2021)MaxRI: A method for discovering maximal rare itemsets2021 4th International Conference on Data Science and Information Technology10.1145/3478905.3478972(334-341)Online publication date: 23-Jul-2021
https://dl.acm.org/doi/10.1145/3478905.3478972
Belaid MLazaar N(2021)Constraint Programming for Itemset Mining with Multiple Minimum Supports2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI52525.2021.00095(598-603)Online publication date: Nov-2021
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Cui YGan WLin HZheng W(2021)FRI-miner: fuzzy rare itemset miningApplied Intelligence10.1007/s10489-021-02574-1Online publication date: 6-Jul-2021
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