A Clustering Algorithm Based On Density With Its Application In The Customer Cluster In The Field Of Telecom

Along with the rapid development of the telecommunications market, telecommunications services are popular among consumers, and telecommunications is gradually showing features of subdivision and diversified. To maintain the leadership position in the market and continuously upgrade customer value, operators must take the initiative to conduct customer segmentation. So how to make effective use of data mining methods on customer segment has a very popular and important application value of the research topic of data mining application.This paper carries a comprehensive comparative study on clustering technology, one of the basic method of data mining, and uses the improved clustering algorithm to segment the telecommunication customers with the purpose of identifying the customers having similar characteristics and being the base foundation of market analysis and market strategy formation.In this paper, the researches and characteristics are:1) Aimed to solve the problem that the density-based clustering algorithm dose not work well when data distribution is not even, a new clustering algorithm based on representatives and point density is provided. The algorithm sets the cluster density with the average density of representative points and sets the k neighbors of representative points as representative region. Under the density of cluster, the points in the representative region which meets the density threshold will be selected as representative point and be reused to adjust the density of cluster. And so repeatedly find out all the representative points and regions. All the region-linked representative points and regions will form a cluster, and any points in no clusters are noises. The experimental results revealed that the algorithm can find any shape and uneven distribution of the density of clusters.2) Although the CBRD algorithm can detect any shape of clusters, but it needs lots of memory and I/O consumption when the data has a large amount, resulting no good application in customer clustering. Therefore, based on the CBRD algorithm, an efficient clustering algorithm based on data overlap is carried in this paper. This algorithm inherits the CBRD clustering algorithm and can find any shape uneven distribution of the density of clusters, also has a high operating efficiency.3) Successfully to applicant the improved density clustering algorithm in telecommunications customer clustering, so that enterprises can better grasp of market dynamics and give effective technical support for mining the potential customers. The experimental result confirms the validity of the clustering algorithm.