Article

Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams

Authors:

James BaileyAuthors Info & Claims

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 307 - 316

https://doi.org/10.1145/1150402.1150438

Published: 20 August 2006 Publication History

Abstract

Patterns of contrast are a very important way of comparing multi-dimensional datasets. Such patterns are able to capture regions of high difference between two classes of data, and are useful for human experts and the construction of classifiers. However, mining such patterns is particularly challenging when the number of dimensions is large. This paper describes a new technique for mining several varieties of contrast pattern, based on the use of Zero-Suppressed Binary Decision Diagrams (ZBDDs), a powerful data structure for manipulating sparse data. We study the mining of both simple contrast patterns, such as emerging patterns, and more novel and complex contrasts, which we call disjunctive emerging patterns. A performance study demonstrates our ZBDD technique is highly scalable, substantially improves on state of the art mining for emerging patterns and can be effective for discovering complex contrasts from datasets with thousands of attributes.

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https://doi.org/10.1051/ita/2024015
Suzuki HIwashita HTakagi TFujishige YHara S(2023)Rule Mining for Correcting Classification Models2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00170(1331-1336)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00170
García-Vico ÁCarmona CGonzález Pdel Jesus M(2023)A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streamsInformation Fusion10.1016/j.inffus.2022.10.02891(412-423)Online publication date: Mar-2023
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Index Terms

Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2006

986 pages

ISBN:1595933395

DOI:10.1145/1150402

Conference Chair:
Tina Eliassi-Rad
LLNL
,
General Chair:
Lyle Ungar
University of Pennsylvania
,
Program Chairs:
Mark Craven
University of Wisconsin
,
Dimitrios Gunopulos
University of California, Riverside

Copyright © 2006 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: 20 August 2006

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KDD06

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KDD06: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 20 - 23, 2006

PA, Philadelphia, USA

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

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Cited By

Abdenbi MBlondin Massé AGoupil ALapointe MLavoie M(2024)Enumerating Minimum Feedback Vertex Sets in directed graphs with union-cat treesRAIRO - Theoretical Informatics and Applications10.1051/ita/202401558(18)Online publication date: 19-Dec-2024
https://doi.org/10.1051/ita/2024015
Suzuki HIwashita HTakagi TFujishige YHara S(2023)Rule Mining for Correcting Classification Models2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00170(1331-1336)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00170
García-Vico ÁCarmona CGonzález Pdel Jesus M(2023)A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streamsInformation Fusion10.1016/j.inffus.2022.10.02891(412-423)Online publication date: Mar-2023
https://doi.org/10.1016/j.inffus.2022.10.028
Fu MDuan LYu Z(2022)Effective Mining of Contrast Hybrid Patterns from Nominal-numerical Mixed DataAdvanced Data Mining and Applications10.1007/978-3-031-22064-7_26(352-367)Online publication date: 24-Nov-2022
https://doi.org/10.1007/978-3-031-22064-7_26
Matsuda KDenzumi SSadakane K(2021)Storing Set Families More Compactly with Top ZDDsAlgorithms10.3390/a1406017214:6(172)Online publication date: 31-May-2021
https://doi.org/10.3390/a14060172
Alipourchavary EErfani SLeckie C(2021)Mining Rare Recurring Events in Network Traffic using Second Order Contrast Patterns2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9533918(1-8)Online publication date: 18-Jul-2021
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Lin YGuan YAsudeh AJagadish H(2020)Identifying insufficient data coverage in databases with multiple relationsProceedings of the VLDB Endowment10.14778/3407790.340782113:12(2229-2242)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.14778/3407790.3407821
Dong G(2019)Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis ProblemsSynthesis Lectures on Data Mining and Knowledge Discovery10.2200/S00897ED1V01Y201901DMK01611:1(1-146)Online publication date: 22-Feb-2019
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Cafaro MPulimeno M(2019)Frequent Itemset MiningBusiness and Consumer Analytics: New Ideas10.1007/978-3-030-06222-4_6(269-304)Online publication date: 31-May-2019
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van Dijk Tvan de Pol J(2018)Multi-core Decision DiagramsHandbook of Parallel Constraint Reasoning10.1007/978-3-319-63516-3_13(509-545)Online publication date: 6-Apr-2018
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