Article

Mining top-K covering rule groups for gene expression data

Authors:

Anthony K. H. Tung,

Xin XuAuthors Info & Claims

SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data

Pages 670 - 681

https://doi.org/10.1145/1066157.1066234

Published: 14 June 2005 Publication History

Abstract

In this paper, we propose a novel algorithm to discover the top-k covering rule groups for each row of gene expression profiles. Several experiments on real bioinformatics datasets show that the new top-k covering rule mining algorithm is orders of magnitude faster than previous association rule mining algorithms.Furthermore, we propose a new classification method RCBT. RCBT classifier is constructed from the top-k covering rule groups. The rule groups generated for building RCBT are bounded in number. This is in contrast to existing rule-based classification methods like CBA [19] which despite generating excessive number of redundant rules, is still unable to cover some training data with the discovered rules. Experiments show that the RCBT classifier can match or outperform other state-of-the-art classifiers on several benchmark gene expression datasets. In addition, the top-k covering rule groups themselves provide insights into the mechanisms responsible for diseases directly.

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Aryabarzan NMinaei-Bidgoli B(2021)A Novel Pruning Strategy for Mining Discriminative PatternsIranian Journal of Science and Technology, Transactions of Electrical Engineering10.1007/s40998-020-00397-3Online publication date: 5-Jan-2021
https://doi.org/10.1007/s40998-020-00397-3
Zhao LChen FYe Y(2020)Efficient Learning with Exponentially-Many Conjunctive Precursors for Interpretable Spatial Event ForecastingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.291218732:10(1923-1935)Online publication date: 1-Oct-2020
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Shang JJiang MTong WXiao JPeng JHan J(2018)DPPred: An Effective Prediction Framework with Concise Discriminative PatternsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.275747630:7(1226-1239)Online publication date: 1-Jul-2018
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Mining top-K covering rule groups for gene expression data
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data

June 2005

990 pages

ISBN:1595930604

DOI:10.1145/1066157

Conference Chair:
Fatma Ozcan
IBM Almaden Research Center

Copyright © 2005 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: 14 June 2005

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SIGMOD/PODS05: International Conference on Management of Data and Symposium on Principles Database and Systems

June 14 - 16, 2005

Maryland, Baltimore

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Aryabarzan NMinaei-Bidgoli B(2021)A Novel Pruning Strategy for Mining Discriminative PatternsIranian Journal of Science and Technology, Transactions of Electrical Engineering10.1007/s40998-020-00397-3Online publication date: 5-Jan-2021
https://doi.org/10.1007/s40998-020-00397-3
Zhao LChen FYe Y(2020)Efficient Learning with Exponentially-Many Conjunctive Precursors for Interpretable Spatial Event ForecastingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.291218732:10(1923-1935)Online publication date: 1-Oct-2020
https://doi.org/10.1109/TKDE.2019.2912187
Shang JJiang MTong WXiao JPeng JHan J(2018)DPPred: An Effective Prediction Framework with Concise Discriminative PatternsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.275747630:7(1226-1239)Online publication date: 1-Jul-2018
https://doi.org/10.1109/TKDE.2017.2757476
Bhatnagar SKumar A(2018)A rule-based classification of short message service type2018 2nd International Conference on Inventive Systems and Control (ICISC)10.1109/ICISC.2018.8398982(1139-1142)Online publication date: Jan-2018
https://doi.org/10.1109/ICISC.2018.8398982
Paul AShill P(2018)New automatic fuzzy relational clustering algorithms using multi-objective NSGA-IIInformation Sciences10.1016/j.ins.2018.03.025448-449(112-133)Online publication date: Jun-2018
https://doi.org/10.1016/j.ins.2018.03.025
Wang SYin Y(2018)Polygene-based evolutionary algorithms with frequent pattern miningFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-6104-312:5(950-965)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s11704-016-6104-3
Ai DPan HLi XGao YHe D(2018)Association rule mining algorithms on high-dimensional datasetsArtificial Life and Robotics10.1007/s10015-018-0437-y23:3(420-427)Online publication date: 1-Sep-2018
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Zaki FZulkurnain N(2018)Frequent Itemset Mining in High Dimensional Data: A ReviewComputational Science and Technology10.1007/978-981-13-2622-6_32(325-334)Online publication date: 28-Aug-2018
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Tung A(2018)Rule-Based ClassificationEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_559(3265-3268)Online publication date: 7-Dec-2018
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Cheng HHan J(2018)Frequent Itemsets and Association RulesEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_171(1536-1541)Online publication date: 7-Dec-2018
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