【摘要】 提出了一种基于群体智能的客户行为分析算法 .首先将客户的消费模式作为平面上的一个点随机分布于平面区域内 ;然后依据基于群体智能的聚类方法 ,选用由小到大的群体相似系数进行聚类分析 ;最后 ,在平面区域内采用递归算法收集聚类结果 ,获得不同消费特征的客户群体 .文中还提出了算法的并行策略 ,提高了算法对大数据量的适应性 .该文以电信移动客户话费数据作为实验数据 ,并将算法结果与其它经典聚类算法的结果进行比较分析 .分析结果表明 :这种基于群体智能的客户行为分析算法能够满足客户聚类和分类的要求 ,特别是在大客户分析及一对一营销中特别客户的分析方面该算法有直观、类别特征明显等特点更多还原

【Abstract】 A customer behavior analysis algorithm based on swarm intelligence is proposed. Firstly, customer consumption patterns are randomly projected on a plane. Then, clustering analysis is processed by a clustering method based on swarm intelligence with different swarm similarity coefficients. Finally, the clustering customer groups with various consume characteristics are collected from the plane by a recursive algorithm. A parallel strategy is also proposed. It improves the scalability of the algorithm. The data of telecom mobile customer consumption are used in the experiment. The results are compared with the results obtained by other clustering methods such as k-means algorithm and self-organizing maps algorithm. The comparison shows that this customer behavior analysis algorithm based on swarm intelligence meets the demands of customer clustering and classifying of customer relationship management. Especially, on the aspect of master customer analysis and one to one sell analysis, the algorithm shows the advantages of visualization, self-organization and clusters with distinct characteristics.更多还原