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Title: Outlier detection in data mining: Exclusion of errors or loss of information?
Language: English
Authors: Hochkamp, Florian 
Rabe, Markus 
Editor: Kersten, Wolfgang  
Jahn, Carlos  
Blecker, Thorsten 
Ringle, Christian M.  
Keywords: Advanced Manufacturing; Industry 4.0
Issue Date: Sep-2022
Publisher: epubli
Source: Hamburg International Conference of Logistics (HICL) 33: 91-117 (2022)
Abstract (english): 
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.
Conference: Hamburg International Conference of Logistics (HICL) 2022 
DOI: 10.15480/882.4689
ISBN: 978-3-756541-95-9
ISSN: 2365-5070
Document Type: Chapter/Article (Proceedings)
Peer Reviewed: Yes
License: CC BY-SA 4.0 (Attribution-ShareAlike 4.0) CC BY-SA 4.0 (Attribution-ShareAlike 4.0)
Part of Series: Proceedings of the Hamburg International Conference of Logistics (HICL) 
Volume number: 33
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