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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

6 Downloads (Pure)

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.

Original language	English
Title of host publication	Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022
Editors	Arindam Banerjee, Zhi-Hua Zhou, Evangelos E. Papalexakis, Matteo Riondato
Publisher	Society for Industrial and Applied Mathematics (SIAM)
Pages	585-593
Number of pages	9
ISBN (Electronic)	978-1-61197-717-2
DOIs	https://doi.org/10.1137/1.9781611977172.66
Publication status	Published - 2022
Event	2022 SIAM International Conference on DataMining, SDM 2022 - Virtual, United States Duration: 28 Apr 2022 → 30 Apr 2022

Conference

Conference	2022 SIAM International Conference on DataMining, SDM 2022
Abbreviated title	SDM 2022
Country/Territory	United States
City	Virtual
Period	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 (Eds.), Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022 (pp. 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. editor / Arindam Banerjee ; Zhi-Hua Zhou ; Evangelos E. Papalexakis ; Matteo Riondato. Society for Industrial and Applied Mathematics (SIAM), 2022. pp. 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.",

author = "Schouten, \{Rianne Margaretha\} and Wouter Duivesteijn and Mykola Pechenizkiy",

year = "2022",

doi = "10.1137/1.9781611977172.66",

language = "English",

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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",

}

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 (eds), Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022. Society for Industrial and Applied Mathematics (SIAM), pp. 585-593, 2022 SIAM International Conference on DataMining, SDM 2022, Virtual, United States, 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. ed. / Arindam Banerjee; Zhi-Hua Zhou; Evangelos E. Papalexakis; Matteo Riondato. Society for Industrial and Applied Mathematics (SIAM), 2022. p. 585-593.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N2 - 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.

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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U2 - 10.1137/1.9781611977172.66

DO - 10.1137/1.9781611977172.66

M3 - Conference contribution

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BT - Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022

A2 - Banerjee, Arindam

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A2 - Papalexakis, Evangelos E.

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Y2 - 28 April 2022 through 30 April 2022

ER -

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

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