research-article

Correlated itemset mining in ROC space: a constraint programming approach

Authors:

Siegfried Nijssen,

Luc De RaedtAuthors Info & Claims

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 647 - 656

https://doi.org/10.1145/1557019.1557092

Published: 28 June 2009 Publication History

Abstract

Correlated or discriminative pattern mining is concerned with finding the highest scoring patterns w.r.t. a correlation measure (such as information gain). By reinterpreting correlation measures in ROC space and formulating correlated itemset mining as a constraint programming problem, we obtain new theoretical insights with practical benefits. More specifically, we contribute 1) an improved bound for correlated itemset miners, 2) a novel iterative pruning algorithm to exploit the bound, and 3) an adaptation of this algorithm to mine all itemsets on the convex hull in ROC space. The algorithm does not depend on a minimal frequency threshold and is shown to outperform several alternative approaches by orders of magnitude, both in runtime and in memory requirements.

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Cited By

Mordvanyuk NBifet ALópez B(2022)VEPRECO: Vertical databases with pre-pruning strategies and common candidate selection policies to fasten sequential pattern miningExpert Systems with Applications10.1016/j.eswa.2022.117517204(117517)Online publication date: Oct-2022
https://doi.org/10.1016/j.eswa.2022.117517
He ZWu ZXu GLiu YZou Q(2021)Decision Tree for SequencesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3075023(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3075023
Wang SCao L(2020)Inferring Implicit Rules by Learning Explicit and Hidden Item DependencyIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2017.276854750:3(935-946)Online publication date: Mar-2020
https://doi.org/10.1109/TSMC.2017.2768547
Show More Cited By

Index Terms

Correlated itemset mining in ROC space: a constraint programming approach
1. Information systems
  1. Information systems applications
    1. Data mining
2. Theory of computation
  1. Logic
    1. Constraint and logic programming

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Published In

cover image ACM Conferences

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

June 2009

1426 pages

ISBN:9781605584959

DOI:10.1145/1557019

General Chairs:
John Elder
Elder Research, Inc., USA
,
Françoise Soulié Fogelman
KXEN, France
,
Program Chairs:
Peter Flach
University of Bristol, UK
,
Mohammed Zaki
RPI, USA

Copyright © 2009 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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Publication History

Published: 28 June 2009

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KDD09

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KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

June 28 - July 1, 2009

Paris, France

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

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Cited By

Mordvanyuk NBifet ALópez B(2022)VEPRECO: Vertical databases with pre-pruning strategies and common candidate selection policies to fasten sequential pattern miningExpert Systems with Applications10.1016/j.eswa.2022.117517204(117517)Online publication date: Oct-2022
https://doi.org/10.1016/j.eswa.2022.117517
He ZWu ZXu GLiu YZou Q(2021)Decision Tree for SequencesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3075023(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3075023
Wang SCao L(2020)Inferring Implicit Rules by Learning Explicit and Hidden Item DependencyIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2017.276854750:3(935-946)Online publication date: Mar-2020
https://doi.org/10.1109/TSMC.2017.2768547
Hämäläinen WWebb G(2019)A tutorial on statistically sound pattern discoveryData Mining and Knowledge Discovery10.1007/s10618-018-0590-x33:2(325-377)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s10618-018-0590-x
Malioutov DMeel K(2018)MLIC: A MaxSAT-Based Framework for Learning Interpretable Classification RulesCardiovascular Computing—Methodologies and Clinical Applications10.1007/978-3-319-98334-9_21(312-327)Online publication date: 23-Aug-2018
https://doi.org/10.1007/978-3-319-98334-9_21
Dzyuba VLeeuwen MRaedt L(2017)Flexible constrained sampling with guarantees for pattern miningData Mining and Knowledge Discovery10.1007/s10618-017-0501-631:5(1266-1293)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s10618-017-0501-6
Schaus PAoga JGuns T(2017)CoverSize: A Global Constraint for Frequency-Based Itemset MiningPrinciples and Practice of Constraint Programming10.1007/978-3-319-66158-2_34(529-546)Online publication date: 23-Aug-2017
https://doi.org/10.1007/978-3-319-66158-2_34
Song WWang CLi J(2016)Binary partition for itemsets expansion in mining high utility itemsetsIntelligent Data Analysis10.3233/IDA-16083820:4(915-931)Online publication date: 15-Jun-2016
https://doi.org/10.3233/IDA-160838
Greco GGuzzo ALupia FPontieri L(2015)Process Discovery under Precedence ConstraintsACM Transactions on Knowledge Discovery from Data10.1145/27100209:4(1-39)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1145/2710020
Budhathoki KVreeken J(2015)The Difference and the Norm — Characterising Similarities and Differences Between DatabasesMachine Learning and Knowledge Discovery in Databases10.1007/978-3-319-23525-7_13(206-223)Online publication date: 29-Aug-2015
https://doi.org/10.1007/978-3-319-23525-7_13
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