In this paper we used RFM technique and various clustering algorithms applied to the real customer data of one of the largest private banks of. Azerbaijan.
Market segmentation is one of the most important area of knowledge-based marketing. In banks it is really a challenging task
The clustering methods employed. K-Means method and K-Medoids method based on RFM score of customer's Internet Banking transactions. This research used.
behavior using clustering algorithms and data mining techniques. Ansari &. Riasi. (2016). Context: Iran. Data from 250 bank.
Analysis of customers using the RFM-model can also be combined with other grouping methods in the clustering algorithm in data mining [8]. [5] did a research
Murlewski“Clustering algorithms for bank customer segmentation
25 de out. de 2021 Clustering is typically the initial step of customer segmentation. Thus the present work seeks to extract efficient features to cluster bank ...
Kuo Ho and Hu (2002) compared three clustering methods for seg- mentation in the 3C (computer
(Nyabwari 2017) Machine learning algorithms are resource efficient and at the same time give real time analysis on the bank's customers
customer behavior in the use of bank card segmentation is done. ... customers