Hybrid data mining method of telecom customer based on improved Kmeans and XGBoost

Abstract

In order to mine specified telecom customers with special behaviours from vast voice communication records of Telecom company, a novel Hybrid Data Mining method of Telecom Customers (HDMTC) with special behaviours is proposed in this paper, which integrates Kmeans and XGBoost into one framework. First, AHP model helped to model the features of customers with special behaviours. Then, Due to semi-supervised methodology, Kmeans approach was improved by small amounts of tagged initial cluster centroids and weighted Euclidean distance. Improved Kmeans was utilized to tag the decision attribute of data to construct training dataset. After that, XGBoost model was trained via the training dataset. Finally, the efficiency of HDMTC was validated via real records from telecom company.