Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS)

Rianne Margaretha Schouten; Wouter Duivesteijn; Mykola Pechenizkiy

doi:10.1137/1.9781611977172.66

Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS)

Rianne Margaretha Schouten
, Wouter Duivesteijn
, Mykola Pechenizkiy

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

3 Citaten (Scopus)

5 Downloads (Pure)

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Repeated Cross-Sectional (RCS) data measures a phenomenon by repeatedly sampling new cases from a population at successive measurement moments. It allows for analyzing societal trends without the need to follow individuals. To gain a deeper understanding of these trends, we propose EMM-RCS, an Exceptional Model Mining instance designed to find subgroups displaying exceptional trend behavior in RCS data. We build quality measures on the standard error, finding various types of exceptionalities within trends (exceptional flattening, slope, deviation from the norm). Additionally, EMM-RCS can handle practical RCS data problems, including uneven spacing of measurements over time, fluctuating sample sizes, and missing data.

Originele taal-2	Engels
Titel	Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022
Redacteuren	Arindam Banerjee, Zhi-Hua Zhou, Evangelos E. Papalexakis, Matteo Riondato
Uitgeverij	Society for Industrial and Applied Mathematics (SIAM)
Pagina's	585-593
Aantal pagina's	9
ISBN van elektronische versie	978-1-61197-717-2
DOI's	https://doi.org/10.1137/1.9781611977172.66
Status	Gepubliceerd - 2022
Evenement	2022 SIAM International Conference on DataMining, SDM 2022 - Virtual, Verenigde Staten van Amerika Duur: 28 apr 2022 → 30 apr 2022

Congres

Congres	2022 SIAM International Conference on DataMining, SDM 2022
Verkorte titel	SDM 2022
Land/Regio	Verenigde Staten van Amerika
Stad	Virtual
Periode	28/04/22 → 30/04/22

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10.1137/1.9781611977172.66

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Schouten, R. M., Duivesteijn, W., & Pechenizkiy, M. (2022). Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS). In A. Banerjee, Z.-H. Zhou, E. E. Papalexakis, & M. Riondato (editors), Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022 (blz. 585-593). Society for Industrial and Applied Mathematics (SIAM). https://doi.org/10.1137/1.9781611977172.66

Schouten, Rianne Margaretha ; Duivesteijn, Wouter ; Pechenizkiy, Mykola. / Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS). Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022. redacteur / Arindam Banerjee ; Zhi-Hua Zhou ; Evangelos E. Papalexakis ; Matteo Riondato. Society for Industrial and Applied Mathematics (SIAM), 2022. blz. 585-593

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title = "Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS)",

abstract = "Repeated Cross-Sectional (RCS) data measures a phenomenon by repeatedly sampling new cases from a population at successive measurement moments. It allows for analyzing societal trends without the need to follow individuals. To gain a deeper understanding of these trends, we propose EMM-RCS, an Exceptional Model Mining instance designed to find subgroups displaying exceptional trend behavior in RCS data. We build quality measures on the standard error, finding various types of exceptionalities within trends (exceptional flattening, slope, deviation from the norm). Additionally, EMM-RCS can handle practical RCS data problems, including uneven spacing of measurements over time, fluctuating sample sizes, and missing data.",

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editor = "Arindam Banerjee and Zhi-Hua Zhou and Papalexakis, \{Evangelos E.\} and Matteo Riondato",

booktitle = "Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022",

publisher = "Society for Industrial and Applied Mathematics (SIAM)",

address = "United States",

note = "2022 SIAM International Conference on DataMining, SDM 2022, SDM 2022 ; Conference date: 28-04-2022 Through 30-04-2022",

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Schouten, RM, Duivesteijn, W & Pechenizkiy, M 2022, Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS). in A Banerjee, Z-H Zhou, EE Papalexakis & M Riondato (reds.), Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022. Society for Industrial and Applied Mathematics (SIAM), blz. 585-593, 2022 SIAM International Conference on DataMining, SDM 2022, Virtual, Verenigde Staten van Amerika, 28/04/22. https://doi.org/10.1137/1.9781611977172.66

Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS). / Schouten, Rianne Margaretha; Duivesteijn, Wouter ; Pechenizkiy, Mykola.
Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022. reds. / Arindam Banerjee; Zhi-Hua Zhou; Evangelos E. Papalexakis; Matteo Riondato. Society for Industrial and Applied Mathematics (SIAM), 2022. blz. 585-593.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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AB - Repeated Cross-Sectional (RCS) data measures a phenomenon by repeatedly sampling new cases from a population at successive measurement moments. It allows for analyzing societal trends without the need to follow individuals. To gain a deeper understanding of these trends, we propose EMM-RCS, an Exceptional Model Mining instance designed to find subgroups displaying exceptional trend behavior in RCS data. We build quality measures on the standard error, finding various types of exceptionalities within trends (exceptional flattening, slope, deviation from the norm). Additionally, EMM-RCS can handle practical RCS data problems, including uneven spacing of measurements over time, fluctuating sample sizes, and missing data.

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Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS)

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