The Application Of A Combination Algorithm Of K-MEANS And SOM In The Telecom Client Segmentation

With the advent of the era of 3G, the competitive situation of the telecommunication industry has drastically changed in China. How should the operators make use of this golden opportunity to make their market share increase effectively and efficiently? Up to now, the question whether the telecom company can conduct rational customer segmentation and accurately target customer demand has become very critical. Fortunately, research and development of Data mining technology can be adopted as an effective approach to the aforementioned question.K-means algorithm is one of the earlier clustering algorithms and has relatively more mature applications in data mining; it can be used to identify the distribution pattern of the data set and therefore has been recognized as an effective clustering algorithm in data mining. Of course it should be acknowledged that K-means algorithm has drawbacks, for examples, the optimal K value should be given before clustering, the random determination of the initial cluster center makes the results uncertain, etc.This paper proposed a S-K algorithm based on the distance cost function, i.e., using a distance cost function in the K-means algorithm for the optimal K value before conducting the clustering; the initial cluster center is obtained using SOM networks rather than being randomly. In the meanwhile, the telecom RFM model is employed to segment telecom customers much more effectively. In sequel, the validity of the proposed algorithm is confirmed through the test of the Iris standard data set. The highlight of the article is the application of S-K algorithm, which is based on the distance cost function, to some telecom company to implement customer segmentation; satisfactory results are derived, which thereby provides more targeted marketing strategies and implications for domestic telecom operation.