Understanding customers' behaviour of telecommunication companies increasing the efficiency of clustering techniques
Ilias Κ. Savvas, Costas Chaikalis, Fabrizio Messina, Dimitrios C. Tselios · 2017
The customers' data of Telecommunication Companies represent a powerful tool to explore their behaviour and then to increase their satisfaction. The data produced is too large to extract on time useful information, which could be beneficial for both sides, companies and customers. One of the solutions to explore this data is representing it by the adoption of clustering techniques. In this work, two distributed / multi-core versions of clustering algorithms were used, namely DBSCAN and k-means, which both of them cluster data according to its characteristics. While DBSCAN is a density-based spatial clustering algorithm and groups data based on the minimum size of participating objects per cluster and the minimum required distance between them, k-means clusters the data objects according the pre-desired number of groups. Thus, since the two methods use different roads to group the data objects, they form different clusters but each one has its importance depending on the characteristics of the applied method. The experiment results of both proposed distributed / multi-core techniques proved their efficiency.