|Publisher URL:||https://www.epubli.de/shop/buch/changing-tides-the-new-role-of-resilience-and-sustainability-in-logistics-and-supply-chain-management-wolfgang-kersten-9783756541959/130939||Title:||Outlier detection in data mining: Exclusion of errors or loss of information?||Language:||English||Authors:||Hochkamp, Florian
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||URI:||http://hdl.handle.net/11420/13905||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)||Part of Series:||Proceedings of the Hamburg International Conference of Logistics (HICL)||Volume number:||33|
|Appears in Collections:||Publications with fulltext|
Show full item record
Files in This Item:
|Hochkamp and Rabe (2022) - Outlier Detection in Data Mining_Exclusion of Errors or Loss of Information.pdf||Outlier Detection in Data Mining_Exclusion of Errors or Loss of Information||848,98 kB||Adobe PDF||View/Open|
checked on Dec 9, 2022
checked on Dec 9, 2022
Note about this record
Cite this record
This item is licensed under a Creative Commons License