research-article

The long and the short of it: summarising event sequences with serial episodes

Authors:

Jilles VreekenAuthors Info & Claims

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 462 - 470

https://doi.org/10.1145/2339530.2339606

Published: 12 August 2012 Publication History

Abstract

An ideal outcome of pattern mining is a small set of informative patterns, containing no redundancy or noise, that identifies the key structure of the data at hand. Standard frequent pattern miners do not achieve this goal, as due to the pattern explosion typically very large numbers of highly redundant patterns are returned.

We pursue the ideal for sequential data, by employing a pattern set mining approach - an approach where, instead of ranking patterns individually, we consider results as a whole. Pattern set mining has been successfully applied to transactional data, but has been surprisingly understudied for sequential data.

In this paper, we employ the MDL principle to identify the set of sequential patterns that summarises the data best. In particular, we formalise how to encode sequential data using sets of serial episodes, and use the encoded length as a quality score. As search strategy, we propose two approaches: the first algorithm selects a good pattern set from a large candidate set, while the second is a parameter-free any-time algorithm that mines pattern sets directly from the data. Experimentation on synthetic and real data demonstrates we efficiently discover small sets of informative patterns.

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Cited By

Gandreti SA. IP. S. S(2024)Breadth-First Search Approach for Mining Serial Episodes with Simultaneous EventsProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632445(36-44)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632445
Zhang AShi WLiu ZZhou X(2024)Geo-SigSPM: mining geographically interesting and significant sequential patterns from trajectoriesInternational Journal of Geographical Information Science10.1080/13658816.2024.232014938:5(879-901)Online publication date: 29-Feb-2024
https://doi.org/10.1080/13658816.2024.2320149
Sebia HGuyet TAudureau E(2024)SWoTTeD: an extension of tensor decomposition to temporal phenotypingMachine Learning10.1007/s10994-024-06545-8113:9(5939-5980)Online publication date: 30-Apr-2024
https://doi.org/10.1007/s10994-024-06545-8
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Index Terms

The long and the short of it: summarising event sequences with serial episodes
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2012

1616 pages

ISBN:9781450314626

DOI:10.1145/2339530

General Chair:
Qiang Yang
Hong Kong University of Science and Technology
,
Program Chairs:
Deepak Agarwal
LinkedIn
,
Jian Pei
Simon Fraser University

Copyright � 2012 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: 12 August 2012

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KDD '12

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KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 16, 2012

Beijing, China

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

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Cited By

Gandreti SA. IP. S. S(2024)Breadth-First Search Approach for Mining Serial Episodes with Simultaneous EventsProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632445(36-44)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632445
Zhang AShi WLiu ZZhou X(2024)Geo-SigSPM: mining geographically interesting and significant sequential patterns from trajectoriesInternational Journal of Geographical Information Science10.1080/13658816.2024.232014938:5(879-901)Online publication date: 29-Feb-2024
https://doi.org/10.1080/13658816.2024.2320149
Sebia HGuyet TAudureau E(2024)SWoTTeD: an extension of tensor decomposition to temporal phenotypingMachine Learning10.1007/s10994-024-06545-8113:9(5939-5980)Online publication date: 30-Apr-2024
https://doi.org/10.1007/s10994-024-06545-8
Schuster MWiegand BVreeken J(2024)Data is Moody: Discovering Data Modification Rules from Process Event LogsMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70344-7_17(285-302)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70344-7_17
Gandreti SP. S. S(2023)Efficient Depth-First Search Approach for Mining Injective General EpisodesProceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)10.1145/3570991.3571012(1-9)Online publication date: 4-Jan-2023
https://dl.acm.org/doi/10.1145/3570991.3571012
Wu JLiu DGuo ZXu QWu Y(2022)TacticFlow: Visual Analytics of Ever-Changing Tactics in Racket SportsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311483228:1(835-845)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3114832
Tseng JHsieh STseng V(2022)A Scalable Analytical Framework for Complex Event Episode Mining With Various Domains ApplicationsIEEE Access10.1109/ACCESS.2022.322896210(130672-130685)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3228962
Galbrun E(2022)The minimum description length principle for pattern mining: a surveyData Mining and Knowledge Discovery10.1007/s10618-022-00846-z36:5(1679-1727)Online publication date: 4-Jul-2022
https://doi.org/10.1007/s10618-022-00846-z
C�ppers JKalofolias JVreeken J(2022)Omen: discovering sequential patterns with reliable prediction delaysKnowledge and Information Systems10.1007/s10115-022-01660-164:4(1013-1045)Online publication date: 5-Mar-2022
https://doi.org/10.1007/s10115-022-01660-1
Alsaudi AAltowim YMehrotra SYu Y(2021)TQELProceedings of the VLDB Endowment10.14778/3476249.347630914:11(2642-2654)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476309
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