Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/306656 
Year of Publication: 
2022
Citation: 
[Journal:] Group Decision and Negotiation [ISSN:] 1572-9907 [Volume:] 31 [Issue:] 6 [Publisher:] Springer Netherlands [Place:] Dordrecht [Year:] 2022 [Pages:] 1203-1234
Publisher: 
Springer Netherlands, Dordrecht
Abstract: 
Systematic pattern recognition as well as the corresponding description of determined patterns entail numerous challenges in the application context of high-dimensional communication data. These can cause increased effort, especially with regard to machine-based processing concerning the determination of regularities in underlying datasets. Due to the increased expansion of dimensions in multidimensional data spaces, determined patterns are no longer interpretable by humans. Taking these challenges into account, this paper investigates to what extent pre-defined communication patterns can be interpreted for the application area of high-dimensional business communication data. An analytical perspective is considered by taking into account a holistic research approach and by subsequently applying selected Machine Learning methods from Association Rule Discovery, Topic Modelling and Decision Trees with regard to the overall goal of semi-automated pattern labelling. The results show that meaningful descriptions can be derived for the interpretation of pre-defined patterns.
Subjects: 
Business Communication Data
Machine Learning
Pattern Recognition
Pattern Labelling
Association Rule Discovery
Topic Modelling
Decision Tree
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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