Conference Papers Year : 2022

Client Segmentation of Mobile Payment Parking Data Using Machine Learning

Ilze Andersone
  • Function : Author
  • PersonId : 1406746
Valdis Bergs
  • Function : Author
Uldis Jansons
  • Function : Author

Abstract

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.
Fichier principal
Vignette du fichier
534967_1_En_37_Chapter.pdf (810.84 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04668658 , version 1 (07-08-2024)

Licence

Identifiers

Cite

Ilze Andersone, Agris Ņikitenko, Valdis Bergs, Uldis Jansons. Client Segmentation of Mobile Payment Parking Data Using Machine Learning. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.450-459, ⟨10.1007/978-3-031-08337-2_37⟩. ⟨hal-04668658⟩
30 View
4 Download

Altmetric

Share

More