Research On Clustering Algorithm And Its Applications In Customer Behavior Analysis

Clustering analysis has become a highly active topic in the data mining research.Its task is the process of grouping the data into classes or clusters so that the objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters.This thesis focuses on two key techniques of cluster analysis and their applications in customer behavior analysis. The two key techniques include initializing cluster centers and outlier detection.The thesis studies the requirements of cluster centers initialization and the current algorithms. Then a dynamic grid generation technology-based algorithm for initializing cluster centers is proposed, which integrate both gird-based and density-based clustering algorithm. This method uses the technology of dynasty grid generation and generates the initial cluster center by calculating the center of gravity of intensive regional connectivity. The simulation results show that, compared with the existing algorithm, DGICC is able to effectively decrease the number of K-means iterations and improve clustering precision. Moreover, the running time of DGICC is nearly approximately linear with respect to the increase in the number of instances and dimensions.At the same time, based on the analysis of the existing outlier detection algorithm and the shortfall of dealing with high-dimensional data, an outlier detection algorithm based on converting cluster is proposed. This method redefines the problem by clustering in the distribution difference space rather than the original feature space. The simulation results show that the new algorithm has better results in finding outliers and the running time than the existing outlier detection algorithms,Based on the new algorithms in this thesis, the paper focuses on the applications of cluster analysis in customer behavior analysis and presents a clustering technology in the telecommunications accounts data analysis and mining.