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Semi-Supervised Churn Clustering for Fault and Constraints Prediction in Telecom Industry
Objective: Churn prediction process on telecom industry is performed using background knowledge such as user information and application oriented constraints with the aim of clustering churn items of telecom communication users. Methods: This paper proposes Semi-supervised Constraint based Churn Clustering (SCCC) method. Semi-supervised learning method identifies different types of churns for labelled data items in telecom industry. PCK Mean based Clusteringwith Expectation Maximization (EM) algorithm finds cluster labels and distance metric for easy grouping of faults set. Similar types of issues are mapped with fast traversal procedure. Constraint based Cluster Membership achieves effective churns clustering by considering relative pairing of users. Findings: The proposed Semi-supervised Constraint based Churn Clustering (SCCC) method is implemented by using JAVA language. This JAVA language uses the code to effectively identify the churns in telecom industry. The SCCC method uses a churn dataset for conducting the experimental work.Experiment is conducted on the factors such as execution time, accuracy, clustering efficiency and support rate on predicting the churns in telecom industry. Experimental results show that the proposed Semi-supervised Constraint based Churn Clustering (SCCC) method out performs than the existing methods. Improvement: Proposed method is able to minimize the execution time, improve accuracy and clustering efficiency and also increase the support rate on predicting churns in telecom industry.
Cluster Membership, Churn Prediction, Expectation Maximization, Semi-Supervised Clustering
- Giannotti F, Lakshmanan LVS, Monreale A, Pedreschi D, Wang HW. Privacy-preserving mining of association rules from outsourced transaction databases.IEEE Systems Journal. 2013 Sep; 7(3):385–95.
- Zhang W, Feng X. Event characterization and prediction based on temporal patterns in dynamic data system.IEEE Transactions on Knowledge and Data Engineering. 2014 Jan; 26(1):144–56.
- Kim YS, Lee H, Johnson JD. Churn management optimization with controllable marketing variables and associated management costs.Expert Systems with Applications.Elsevier. 2013 May; 40(6):2198–207.
- Migueis VL, Poel DVD, Camanho AS, Cunha JFEC. Modeling partial customer churn: On the value of first productcategorypurchase sequences.Expert Systems with Applications. 2012 Sep; 39(12):11250–56.
- Abbasimehr H, Setak M, Tarokh M.A comparative assessment of the performance ofensemble learning in customer churn prediction.The International Arab Journal of Information Technology. 2014 Nov; 11(6):599–606.
- Faris H, Shboul BA, Ghatasheh N.A Genetic Programming based frameworkfor churn prediction in telecommunicationindustry. Springer. 2014 Jun; 8733(6):353–62.
- Adwan O, Faris H, Jaradat K, Harfoushi O, Ghatasheh N.Predicting customer churn in telecom industry using multilayer preceptron neural networks: Modelingand analysis. Life Science Journal. 2014; 11(3):75–81.
- Chueh HE.Analysis of marketing data to extract key factors oftelecom churn management.African Journal of Business Management. 2011 Sep; 5(20):8242–7.
- Radosavljevik D, Putten PVD.Preventing churn in telecommunications: The forgotten network.Springer. 2013; 8207:357–68.
- Faris H.Neighborhood cleaning rules and particle swarm optimization for predicting customer churn behavior in telecom industry.International Journal of Advanced Science and Technology.2014; 68:11–22.
- Pushpa,Shobha G.An efficient method of building the telecom social network for churn prediction.International Journal of Data Mining and Knowledge Management Process (IJDKP). 2012 May; 2(3):31–9.
- Shaaban E, Helmy Y, Khedr A, Nasr M.A proposed churn prediction model.IJERA. 2012 Jun-Jul; 2(4):693–97.ISSN: 2248-9622.www.ijera.com
- Alawin A, Al-ma’aitah M. Proposed ranking for point of sales using data mining for telecom operators.IJDMS. 2014 Jun; 6(3):17–31.
- Trnka A. Position of retraining churn data mining model in six sigma methodology.Proceedings of the World Congress on Engineering and Computer Science. 2012; 2200(1):488– 92.
- Khan I, Usman I, Usman T, Rehman GU, Rehman AU.Intelligent churn prediction for telecommunication industry.International Journal of Innovation and Applied Studies.2013 Sep; 4(1):165–70.
- Obiedat R, Alkasassbeh M, Faris H,Harfoushi O. Customer churn prediction using a hybrid genetic programming approach.Scientific Research and Essays. 2013 Jul; 8(27):1289–95.
- Nabavi S, Jafari S. Providing a customer churn prediction model using random forest and boosted tree techniques. Journal of Basic and Applied Scientific Research. 2013 Jun; 3(6):1018–26.
- Jadhav RJ, Pawar UT. Churn prediction in telecommunication using data mining technology.International Journal of Advanced Computer Science and Applications. 2011 Feb; 2(2):17–9.
- Sharma A, Panigrahi PK. A neural network based approach for predicting customer churn in in cellular network services. International Journal of Computer Applications (0975-8887). 2011 Aug; 27(11):26–31.
- DhandayudamP, Krishnamurthi I. Customer behavior analysis using rough set approach.Journal of Theoretical and Applied Electronic Commerce Research.2013 Aug; 8(2):21–33.
- Yadav K, Tiwari S, Divekar R. Impact of technological changes in Telecom Sector in India.Indian Journal of Science and Technology. 2015 Feb; 8(S4):194–9.
- Sujata J, Sohag S, Tanu D, Chintan D, Shubham P, Sumit G. Impact of Over the Top (OTT) services on Telecom Service Providers.Indian Journal of Science and Technology.2015 Feb; 8(S4):145–60.
- Karamizadeh F, Zolfagharifar SA. Using the clustering algorithms and rule-based of data mining to identify affecting factors in the profit and loss of third party insurance, insurance company auto. Indian Journal of Science and Technology. 2016 Feb; 9(7):1–9.
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