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Polynomial association rules with applications to logistic regression

Author:

Szymon JaroszewiczAuthors Info & Claims

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

Pages 586 - 591

https://doi.org/10.1145/1150402.1150472

Published: 20 August 2006 Publication History

Abstract

A new class of associations (polynomial itemsets and polynomial association rules) is presented which allows for discovering nonlinear relationships between numeric attributes without discretization. For binary attributes, proposed associations reduce to classic itemsets and association rules. Many standard association rule mining algorithms can be adapted to finding polynomial itemsets and association rules. We applied polynomial associations to add non-linear terms to logistic regression models. Significant performance improvement was achieved over stepwise methods, traditionally used in statistics, with comparable accuracy.

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Cited By

Genga LAllodi LZannone N(2022)Association Rule Mining Meets Regression Analysis: An Automated Approach to Unveil Systematic Biases in Decision-Making ProcessesJournal of Cybersecurity and Privacy10.3390/jcp20100112:1(191-219)Online publication date: 21-Mar-2022
https://doi.org/10.3390/jcp2010011
Calders T(2014)Introduction to Pattern MiningBusiness Intelligence10.1007/978-3-319-05461-2_1(1-32)Online publication date: 2014
https://doi.org/10.1007/978-3-319-05461-2_1
Changpetch PLin D(2013)Model selection for logistic regression via association rules analysisJournal of Statistical Computation and Simulation10.1080/00949655.2012.66223183:8(1415-1428)Online publication date: Aug-2013
https://doi.org/10.1080/00949655.2012.662231
Show More Cited By

Index Terms

Polynomial association rules with applications to logistic regression
1. Information systems
  1. Information systems applications
    1. Data mining

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Information

Published In

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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Association for Computing Machinery

New York, NY, United States

Publication History

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

Acceptance Rates

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

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KDD '25

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Genga LAllodi LZannone N(2022)Association Rule Mining Meets Regression Analysis: An Automated Approach to Unveil Systematic Biases in Decision-Making ProcessesJournal of Cybersecurity and Privacy10.3390/jcp20100112:1(191-219)Online publication date: 21-Mar-2022
https://doi.org/10.3390/jcp2010011
Calders T(2014)Introduction to Pattern MiningBusiness Intelligence10.1007/978-3-319-05461-2_1(1-32)Online publication date: 2014
https://doi.org/10.1007/978-3-319-05461-2_1
Changpetch PLin D(2013)Model selection for logistic regression via association rules analysisJournal of Statistical Computation and Simulation10.1080/00949655.2012.66223183:8(1415-1428)Online publication date: Aug-2013
https://doi.org/10.1080/00949655.2012.662231
Fedotenkov I(2013)Consistency of the estimator of binary response models based on AUC maximizationStatistical Methods & Applications10.1007/s10260-013-0229-422:3(381-390)Online publication date: 2-Feb-2013
https://doi.org/10.1007/s10260-013-0229-4
Jaroszewicz S(2008)Minimum variance associationsProceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining10.5555/1786574.1786594(172-183)Online publication date: 20-May-2008
https://dl.acm.org/doi/10.5555/1786574.1786594
Jaroszewicz S(2008)Minimum Variance Associations — Discovering Relationships in Numerical DataAdvances in Knowledge Discovery and Data Mining10.1007/978-3-540-68125-0_17(172-183)Online publication date: 2008
https://doi.org/10.1007/978-3-540-68125-0_17
Huang JDai BChen M(2007)TwainACM Transactions on Knowledge Discovery from Data10.1145/1267066.12670691:2(8-es)Online publication date: 1-Aug-2007
https://dl.acm.org/doi/10.1145/1267066.1267069
Calders TJaroszewicz S(2007)Efficient AUC Optimization for ClassificationKnowledge Discovery in Databases: PKDD 200710.1007/978-3-540-74976-9_8(42-53)Online publication date: 2007
https://doi.org/10.1007/978-3-540-74976-9_8
Fedotenkov I(undefined)Consistency of AUC Maximization as an Estimator of Binary Choice ModelsSSRN Electronic Journal10.2139/ssrn.1991051
https://doi.org/10.2139/ssrn.1991051

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