International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 33 | Number 1 | Year 2016 | Article Id. IJCTT-V33P113 | DOI : https://doi.org/10.14445/22312803/IJCTT-V33P113

Data Mining Evolutionary Learning (DMEL) using H base

Miss. Mansi Shah, Ms. Seema Kolkur

Citation :

Miss. Mansi Shah, Ms. Seema Kolkur, "Data Mining Evolutionary Learning (DMEL) using H base," International Journal of Computer Trends and Technology (IJCTT), vol. 33, no. 1, pp. 60-64, 2016. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V33P113

Abstract

In current market scenarios, telecom companies are quite competitive and look forward to have lion’s share in the market by winning new and withholding existing customers. Customers who are lost to competitor are known as Churned customers and can be retain by adopting Churn prevention model. For a given dataset, this model predicts the list of customers to be churned in future enabling the respective authorities to take action accordingly. However in telecom, the results of algorithms suffer due to disproportion nature and vast size of datasets. In this paper, Genetic Programming (GP) based approach for modelling the challenging problem of churn prediction is incorporated in HBASE. A data mining algorithm, Data Mining Evolutionary Learnings (DMEL), handles a classification problem which helps to meet accuracy of prediction. As data in telecom industry is going to increase so to make the classification process fast DMEL algorithm is incorporated in HBase. For competitive telecom industry, churn prediction approach would be significantly beneficial.

Keywords

telecom industry, churn prediction, genetic algorithms, DMEL, HBase.

References

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