Designing Combo Recharge Plans for Telecom Subscribers Using Itemset Mining Technique

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Shrutik Patil, Ramchandra Karad, Mukesh Dhadse, Vaibhav Jawanjal, Pratvina Talele


Now a days Machine Learning has become an integral part of human research. People are tending to select more automatic system rather than going with the manual handling. Data mining has the huge effect on business analysis as all business relies on their behaviour of customers. Mining the behaviour of customers can help the very existence of the company. This paper has proposed the way to satisfy customers in telecommunication market by knowing the customer’s recharge pattern. It can enhance their will to use the same service provider. By mining the recharge pattern of individual customer, this system will help telecom service providers to prepare combo plans, which will indeed be less than the individual recharges. For mining such kind of data, we are using FP Growth algorithm, it allows frequent item set discovery without candidate item set generation. FP Growth is two step approach, first it builds a compact data structure called the FP-tree and then Extracts frequent item sets directly from the FP-tree.

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How to Cite
, S. P. R. K. M. D. V. J. P. T. “Designing Combo Recharge Plans for Telecom Subscribers Using Itemset Mining Technique”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6, no. 4, Apr. 2018, pp. 204-6, doi:10.17762/ijritcc.v6i4.1545.