Client Segmentation of Mobile Payment Parking Data Using Machine Learning
8th IFIP WG 12.5 International Conference (AIAI 2022). IFIP Advances in Information and Communication Technology. Vol.647
2022
Agris Ņikitenko,
Ilze Andersone,
Uldis Jansons,
Valdis Bergs
This paper addresses the analysis of mobile payment parking data for client segmentation. The transaction data transformation into client-specific attributes is performed from the company data set to achieve the goal. Two clustering algorithms – K-Means and DBScan – are compared for multiple data subsets. For the clustering result interpretation, decision tree representation is used. As a result, the most appropriate combination of the clustering algorithm, its parameters and attribute combination is determined.
Keywords
Client segmentation, Clustering, Mobile payments, Parking
DOI
10.1007/978-3-031-08337-2_37
Hyperlink
https://link.springer.com/chapter/10.1007/978-3-031-08337-2_37
Ņikitenko, A., Andersone, I., Jansons, U., Bergs, V. Client Segmentation of Mobile Payment Parking Data Using Machine Learning. In: 8th IFIP WG 12.5 International Conference (AIAI 2022). IFIP Advances in Information and Communication Technology. Vol.647, Greece, Hersonissos, 17-19 June, 2022. Cham: Springer, 2022, pp.450-459. ISBN 978-3-031-08336-5. ISSN 1868-4238. e-ISSN 1868-422X. Available from: doi:10.1007/978-3-031-08337-2_37
Publication language
English (en)