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Sequence mining in categorical domains: incorporating constraints

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Mohammed J. ZakiAuthors Info & Claims

CIKM '00: Proceedings of the ninth international conference on Information and knowledge management

Pages 422 - 429

https://doi.org/10.1145/354756.354849

Published: 06 November 2000 Publication History

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Cited By

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Zhang YPaquette LBosch N(2024)Using Permutation Tests to Identify Statistically Sound and Nonredundant Sequential Patterns in Educational Event SequencesJournal of Educational and Behavioral Statistics10.3102/10769986241248772Online publication date: 9-May-2024
https://doi.org/10.3102/10769986241248772
Gao LWang CWu G(2023)Hidden Semi-Markov Models-Based Visual Perceptual State Recognition for PilotsSensors10.3390/s2314641823:14(6418)Online publication date: 14-Jul-2023
https://doi.org/10.3390/s23146418
Cagliero LCanale LFarinetti L(2023)Data-Driven Analysis of Student Engagement in Time-Limited Computer LaboratoriesAlgorithms10.3390/a1610046416:10(464)Online publication date: 2-Oct-2023
https://doi.org/10.3390/a16100464
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Index Terms

Sequence mining in categorical domains: incorporating constraints
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

CIKM '00: Proceedings of the ninth international conference on Information and knowledge management

November 2000

532 pages

ISBN:1581133200

DOI:10.1145/354756

Chairmen:
Arvin Agah
Univ. of Kansas, Lawrence
,
Jamie Callan
Carnegie Mellon Univ., Pittsburgh, PA
,
Elke Rundensteiner
Worcester Polytechnic Institute, Worcester, MA
,
Susan Gauch
Univ. of Kansas, Lawrence

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Published: 06 November 2000

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CIKM00: Ninth International Conference on Information and Knowledge Management

November 6 - 11, 2000

Virginia, McLean, USA

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Cited By

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Zhang YPaquette LBosch N(2024)Using Permutation Tests to Identify Statistically Sound and Nonredundant Sequential Patterns in Educational Event SequencesJournal of Educational and Behavioral Statistics10.3102/10769986241248772Online publication date: 9-May-2024
https://doi.org/10.3102/10769986241248772
Gao LWang CWu G(2023)Hidden Semi-Markov Models-Based Visual Perceptual State Recognition for PilotsSensors10.3390/s2314641823:14(6418)Online publication date: 14-Jul-2023
https://doi.org/10.3390/s23146418
Cagliero LCanale LFarinetti L(2023)Data-Driven Analysis of Student Engagement in Time-Limited Computer LaboratoriesAlgorithms10.3390/a1610046416:10(464)Online publication date: 2-Oct-2023
https://doi.org/10.3390/a16100464
Le HYamada THonda YSakamoto TMatsuo RYamazaki TAraki KYokota H(2023)Methods for Analyzing Medical-Order Sequence Variants in Sequential Pattern Mining for Electronic Medical Record SystemsACM Transactions on Computing for Healthcare10.1145/35618254:1(1-28)Online publication date: 30-Mar-2023
https://dl.acm.org/doi/10.1145/3561825
Chesani FFrancescomarino CGhidini CLoreti DMaggi FMello PMontali MTessaris S(2023)Process Discovery on Deviant Traces and Other Stranger ThingsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323220735:11(11784-11800)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TKDE.2022.3232207
Mistry SGouripeddi RRaman VFacelli J(2023)Sequential data mining of infection patterns as predictors for onset of type 1 diabetes in genetically at-risk individualsJournal of Biomedical Informatics10.1016/j.jbi.2023.104385142(104385)Online publication date: Jun-2023
https://doi.org/10.1016/j.jbi.2023.104385
Verstege SZhang YWierenga PPaquette LDiederen J(2023)Using Sequential Pattern Mining to Understand How Students Use Guidance While Doing Scientific CalculationsTechnology, Knowledge and Learning10.1007/s10758-023-09677-329:2(897-920)Online publication date: 16-Aug-2023
https://doi.org/10.1007/s10758-023-09677-3
Zhang YPaquette L(2023)Sequential Pattern Mining in Educational Data: The Application Context, Potential, Strengths, and LimitationsEducational Data Science: Essentials, Approaches, and Tendencies10.1007/978-981-99-0026-8_6(219-254)Online publication date: 30-Apr-2023
https://doi.org/10.1007/978-981-99-0026-8_6
Nguyen H(2022)Let's teach Kibot: Discovering discussion patterns between student groups and two conversational agent designsBritish Journal of Educational Technology10.1111/bjet.13219Online publication date: 8-Apr-2022
https://doi.org/10.1111/bjet.13219
Daraio ECagliero LChiusano SGarza P(2022)Complementing Location-Based Social Network Data With Mobility Data: A Pattern-Based ApproachIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.318256923:11(21216-21227)Online publication date: Nov-2022
https://doi.org/10.1109/TITS.2022.3182569
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Mining Optimized Association Rules with Categorical and Numeric Attributes

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