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Outlier detection in data mining: Exclusion of errors or loss of information?
Citation Link: https://doi.org/10.15480/882.4689
Publikationstyp
Conference Paper
Date Issued
2022-09
Sprache
English
Author(s)
Herausgeber*innen
TORE-DOI
First published in
Number in series
33
Start Page
91
End Page
117
Citation
Hamburg International Conference of Logistics (HICL) 33: 91-117 (2022)
Contribution to Conference
Publisher
epubli
Peer Reviewed
true
Purpose: Our research emphasizes the importance of considering outliers in production logistics tasks. With a growing amount of data, we require data mining to cope with these tasks. We underline that the widespread exclusion of outliers in data pre-processing for data mining leads to a loss of information and that using outlier interpretation can be used to address the issue.
Methodology: The paper discusses the data pre-processing of data mining in production logistics problems. Methods of outlier interpretation are collected based on a literature review. In addition to the literature-based investigation, the work relies on a case study that illustrates the individual evaluation of outliers.
Findings: This work shows that outliers take a special focus on the information generation. Within data pre-processing, a distinction must be made between an outlier as a defect and an outlier as a special datum. This can be conducted by methods presented in the literature.
Originality: This paper adds to existing literature in the research field of insufficiently analyzed outlier interpretation and shows a need for research in data pre-processing of data mining.
Methodology: The paper discusses the data pre-processing of data mining in production logistics problems. Methods of outlier interpretation are collected based on a literature review. In addition to the literature-based investigation, the work relies on a case study that illustrates the individual evaluation of outliers.
Findings: This work shows that outliers take a special focus on the information generation. Within data pre-processing, a distinction must be made between an outlier as a defect and an outlier as a special datum. This can be conducted by methods presented in the literature.
Originality: This paper adds to existing literature in the research field of insufficiently analyzed outlier interpretation and shows a need for research in data pre-processing of data mining.
Subjects
Advanced Manufacturing; Industry 4.0
DDC Class
004: Informatik
380: Handel, Kommunikation, Verkehr
Publication version
publishedVersion
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Hochkamp and Rabe (2022) - Outlier Detection in Data Mining_Exclusion of Errors or Loss of Information.pdf
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