Marketers use segmentation as an important tool to better understand and effectively target customers by adopting marketing strategies catering to the needs and characteristics of each segment. Traditionally, customer segmentation using clustering algorithms is performed using a single attribute set. However, there may be multiple meaningful natural customer groupings possible if independent subsets of attributes were considered while discovering the clusters. Multi-view clustering provides a meaningful way to combine groupings in different feature subsets by assigning customers to multiple segments, representing different perspectives of customer behavior. In this paper we propose a novel method for performing multi-view clustering wherein multiple groupings are generated using a non-parametric clustering algorithm and are then combined and visualized using a cross-tab/sunburst based visualization scheme. We also demonstrate the effectiveness of the proposed approach by applying it to a variety of real-world problems related to mobile subscriber segmentation. © 2016 IEEE.