Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287304 
Year of Publication: 
2021
Citation: 
[Journal:] Group Decision and Negotiation [ISSN:] 1572-9907 [Volume:] 31 [Issue:] 3 [Publisher:] Springer Netherlands [Place:] Dordrecht [Year:] 2021 [Pages:] 555-589
Publisher: 
Springer Netherlands, Dordrecht
Abstract: 
The systematic processing of unstructured communication data as well as the milestone of pattern recognition in order to determine communication groups in negotiations bears many challenges in Machine Learning. In particular, the so-called curse of dimensionality makes the pattern recognition process demanding and requires further research in the negotiation environment. In this paper, various selected renowned clustering approaches are evaluated with regard to their pattern recognition potential based on high-dimensional negotiation communication data. A research approach is presented to evaluate the application potential of selected methods via a holistic framework including three main evaluation milestones: the determination of optimal number of clusters, the main clustering application, and the performance evaluation. Hence, quantified Term Document Matrices are initially pre-processed and afterwards used as underlying databases to investigate the pattern recognition potential of clustering techniques by considering the information regarding the optimal number of clusters and by measuring the respective internal as well as external performances. The overall research results show that certain cluster separations are recommended by internal and external performance measures by means of a holistic evaluation approach, whereas three of the clustering separations are eliminated based on the evaluation results.
Subjects: 
Communication data
Pattern recognition
Clustering
Optimisation
High-dimensional data
Term document matrix
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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