Customer segmentation model based on two-step optimization in big data era

DOI

10.2991/icitmi-15.2015.133How to use a DOI?

Keywords

Big data, customer segmentation, two-step optimization model, data mining

Abstract

With the advent of the era of big datasets, real-time data is becoming increasingly important in assisting the decision making process for commercial banks. In this paper, we develop a two-step optimization model (FSGA-FCEN) based on genetic algorithm (GA) and cluster ensemble (CE) for customer segmentation. Firstly, the key attributes are selected using GA. Then FCEN algorithm is used to segment customers into small groups. Taking 3544 customers in a commercial bank as samples, empirical results show that, compared with K-means, FCM and MAJ models, two-step model is an efficient and practical tool for customer segmentation.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wei Gao
AU  - Huiting Jia
AU  - Ruzhen Yan
PY  - 2015/10
DA  - 2015/10
TI  - Customer segmentation model based on two-step optimization in big data era
BT  - Proceedings of the 4th International Conference on Information Technology and Management Innovation
PB  - Atlantis Press
SP  - 800
EP  - 803
SN  - 2352-538X
UR  - https://doi.org/10.2991/icitmi-15.2015.133
DO  - 10.2991/icitmi-15.2015.133
ID  - Gao2015/10
ER  -