Paper Info
Title
Detecting GSM churners by using Euclidean Indexing HDMR.
Abstract
Preventing GSM subscribers to move to another operator is an important and crucial issue for the GSM operators. Churn management is of essential importance in detecting loyal and hopeless subscribers. Keeping current GSM number when changing the GSM operator also facilitates these subscribers to switch to another operator. Euclidean Indexing High Dimensional Model Representation (HDMR) method is a polynomial based modeling method which is used to predict the churner behavior of the GSM subscribers. An up-to-date data set consists of demographic information and call details records with the related churn behavior is used to model the churner detection problem. The proposed method uses 640 randomly selected training nodes for the modeling process while 316 nodes are used to examine the performance of the proposed method and to make comparisons with the data mining techniques. (C) 2014 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2015
10.1016/j.asoc.2014.11.001
Applied Soft Computing
Keywords
Field
DocType
Churn Management,Telecom Churn Prediction,Polynomial based Classification,Multivariate Data Partitioning,HDMR
Data mining,GSM,Polynomial,Computer science,Search engine indexing,Theoretical computer science,Artificial intelligence,Operator (computer programming),Euclidean geometry,High-dimensional model representation,Machine learning
Journal
Volume
Issue
ISSN
27
C
1568-4946
Citations 
PageRank 
References 
0
0.34
17
Authors
2
Name
Order
Citations
PageRank
M. Alper Tunga1405.44
Adem Karahoca29715.26