Market segmentation using high-dimensional sparse consumers data
作者：
Highlights：
• A methodology integrating RFM with the sparse K-means clustering algorithm is considered.
• Our approach is suitable for handling large, high-dimensional and sparse consumer data.
• The Chinese mobile telecommunications market is used as an empirical implementation.
• The results are robust when compared with the biclustering of customers method.
• A useful tool and valid methodology for decision makers in general.
摘要
•A methodology integrating RFM with the sparse K-means clustering algorithm is considered.•Our approach is suitable for handling large, high-dimensional and sparse consumer data.•The Chinese mobile telecommunications market is used as an empirical implementation.•The results are robust when compared with the biclustering of customers method.•A useful tool and valid methodology for decision makers in general.
论文关键词：Precision marketing,RFM theory,Sparse K-means algorithm,BCBimax algorithm,Mobile telecommunications industry
论文评审过程：Received 15 May 2019, Revised 24 September 2019, Accepted 13 December 2019, Available online 14 December 2019, Version of Record 6 January 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.113136
