Application In Customers Consumption Model Of Telecom Based On Clustering Algorithm And Neural Network

Data mining, also referred to Knowledge Discovery in Database (KDD), means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in database. It is an emerging interdisciplinary studies, collects kinds of research results such as Machine Learning, Pattern Recognition, Database, Statistics, Artificial Intelligence and so on. Clustering and Neural Network are two of the most common algorithms in Data Mining. This paper has mainly studied the K-means clustering algorithm and the BP neural network, and unifies them applies in the research of telecommunication customer consumption pattern.The clustering is the process which divides the data set into certain group of (class) or the kind of (cluster), and enables the data object in the identical group to be similar, but the different groups data object are in a big difference. The K-means algorithm is one of.main clustering algorithms. It is a kind of the clustering algorithms based on partitioning methods. This algorithm selects K (K is stochastically cluster number) the spots as the center of the initial cluster, completes the cluster through an iterative process. If the initial cluster center is selected unreasonably, which will mislead the cluster process, and come into an unreasonable result. This paper analyses and studies the selection methods of initial numbers in K-means Clustering, proposed one new method to select initial cluster center, and enhanced the cluster rate of accuracy.In addition, the BP algorithm, which is the most common neural network algorithm, also is one of key research in this paper. Although the result of the BP. network forecast model is good, but pure BP algorithm has some insufficiencies: (1) easy to fall into the partial extreme value; (2) the tendency in forgetting studied samples; (3) study efficiency is not high, convergence rate slow and so on. This paper will simulate the annealing (SA) algorithm to optimize the BP network, avoided the BP algorithm convergence rate being slow well, easy to fall into the partial extreme point question. It analyzed through the experiment, and has obtained better-forecast effect.Therefore, this paper first uses in statistics correlation analysis method to eliminate the redundant field of the model, and then establishes a classification forecast model based on Clustering analysis and Neural Network algorithms, and will establish the classified model to apply in the telecommunication customer consumption pattern, confirms the algorithm the feasibility and the validity. Thus can help the customer servicers to provide the corresponding service according to the customer consumer behavior characteristic and to adopt the pointed marketing strategy. According to the latent customer expense pattern, it may provide a better service to the existing customers, and select the latent customer and their demand, finally brings a bigger profit for the company.