Open AccessHybrid Data Mining Method of Telecom Customer Based on Improved Kmeans and XGBoost
Author(s) -
Yongge Shi,
Shaoyun Yan,
Meibin He,
Xiangjun Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2010/1/012060
Subject(s) - k means clustering , computer science , euclidean distance , analytic hierarchy process , centroid , data mining , telecommunications , artificial intelligence , cluster analysis , operations research , mathematics
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.