article

Maximal exceptions with minimal descriptions

Author:

Matthijs LeeuwenAuthors Info & Claims

Data Mining and Knowledge Discovery, Volume 21, Issue 2

Pages 259 - 276

https://doi.org/10.1007/s10618-010-0187-5

Published: 01 September 2010 Publication History

Abstract

We introduce a new approach to Exceptional Model Mining. Our algorithm, called EMDM, is an iterative method that alternates between Exception Maximisation and Description Minimisation. As a result, it finds maximally exceptional models with minimal descriptions. Exceptional Model Mining was recently introduced by Leman et al. (Exceptional model mining 1---16, 2008) as a generalisation of Subgroup Discovery. Instead of considering a single target attribute, it allows for multiple `model' attributes on which models are fitted. If the model for a subgroup is substantially different from the model for the complete database, it is regarded as an exceptional model. To measure exceptionality, we propose two information-theoretic measures. One is based on the Kullback---Leibler divergence, the other on Krimp. We show how compression can be used for exception maximisation with these measures, and how classification can be used for description minimisation. Experiments show that our approach efficiently identifies subgroups that are both exceptional and interesting.

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Digital Library

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Siebes A, Vreeken J, van Leeuwen M (2006) Item sets that compress. In: Proceedings of the SDM'06, pp 393-404.

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Cited By

Lipman TMilo TSomech ADas SPandis ISelçuk Candan KAmer-Yahia S(2023)ATENA-PRO: Generating Personalized Exploration Notebooks with Constrained Reinforcement LearningCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589727(167-170)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1145/3555041.3589727
Deutch DGilad AMilo TMualem ASomech A(2022)FEDEXProceedings of the VLDB Endowment10.14778/3565838.356584115:13(3854-3868)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.14778/3565838.3565841
Proença HGrünwald PBäck Tvan Leeuwen M(2022)Robust subgroup discoveryData Mining and Knowledge Discovery10.1007/s10618-022-00856-x36:5(1885-1970)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s10618-022-00856-x
Show More Cited By

Index Terms

Maximal exceptions with minimal descriptions
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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cover image Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery Volume 21, Issue 2

September 2010

123 pages

ISSN:1384-5810

Issue’s Table of Contents

Copyright © Copyright © 2010 The Author(s).

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Kluwer Academic Publishers

United States

Publication History

Published: 01 September 2010

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Cited By

Lipman TMilo TSomech ADas SPandis ISelçuk Candan KAmer-Yahia S(2023)ATENA-PRO: Generating Personalized Exploration Notebooks with Constrained Reinforcement LearningCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589727(167-170)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1145/3555041.3589727
Deutch DGilad AMilo TMualem ASomech A(2022)FEDEXProceedings of the VLDB Endowment10.14778/3565838.356584115:13(3854-3868)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.14778/3565838.3565841
Proença HGrünwald PBäck Tvan Leeuwen M(2022)Robust subgroup discoveryData Mining and Knowledge Discovery10.1007/s10618-022-00856-x36:5(1885-1970)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s10618-022-00856-x
Deutch DGilad AMilo TSomech A(2020)ExplainEDProceedings of the VLDB Endowment10.14778/3415478.341550813:12(2917-2920)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.14778/3415478.3415508
Bar El OMilo TSomech AMaier DPottinger RDoan ATan WAlawini ANgo H(2020)Automatically Generating Data Exploration Sessions Using Deep Reinforcement LearningProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3389779(1527-1537)Online publication date: 11-Jun-2020
https://dl.acm.org/doi/10.1145/3318464.3389779
Proença HGrünwald PBäck TLeeuwen M(2020)Discovering Outstanding Subgroup Lists for Numeric Targets Using MDLMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-67658-2_2(19-35)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-67658-2_2
Bendimerad APlantevit MRobardet C(2018)Mining exceptional closed patterns in attributed graphsKnowledge and Information Systems10.1007/s10115-017-1109-256:1(1-25)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s10115-017-1109-2
Lemmerich FBecker MSinger PHelic DHotho AStrohmaier MKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)Mining Subgroups with Exceptional Transition BehaviorProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939752(965-974)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2939672.2939752
Duivesteijn WFeelders AKnobbe A(2016)Exceptional Model MiningData Mining and Knowledge Discovery10.1007/s10618-015-0403-430:1(47-98)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1007/s10618-015-0403-4
Luna JPechenizkiy MVentura S(2016)Mining exceptional relationships with grammar-guided genetic programmingKnowledge and Information Systems10.1007/s10115-015-0859-y47:3(571-594)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s10115-015-0859-y
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