Comparison of three data mining algorithms for potential 4G customers prediction

Chun Gui, Qiang Lin


The size and number of telecom databases are growing quickly but most of the data has not been analyzed for revealing thehidden and valuable intellectual. Models developed from data mining techniques are useful for telecom to make right prediction.The dataset contains one million customers from a telecom company. We implement data mining techniques, i.e., AdaboostM1(ABM) algorithm, Naïve Bayes (NB) algorithm, Local Outlier Factor (LOF) algorithm to develop the predictive models. Thispaper studies the application of data mining techniques to develop 4G customer predictive models and compares three models onour dataset through precision, recall, and cumulative recall curve. The result is that precision of ABM, NB and LOF are 0.6016,0.6735 and 0.3844. From the aspects of cumulative recall curve NB algorithm also is the best one.

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Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

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