摘要
利用客户关系图可以很清晰地看出企业与客户之间的各类关系,便于企业决策者采取针对性的措施来改善客户关系。该文提出了一种基于改进的FP-Growth算法进行客户关系图提取的方法,通过最小支持度寻找到所有的频繁项集,然后结合最小置信度,筛选出所需要的关联规则来提高算法的效率。本方法已应用于浙江中烟CRM系统,结果证明该改进算法有比较好的效果。
Customer relationships can be clearly seen in customer relationship graph,thus business decision-makers can take specific measures to facilitate customer relationships.This paper presents an improved algorithm based on FP-Growth algorithm to extract customer relationship graph.We find all frequent itemsets through minimum support,then filter out the desired association rules integrated with the minimum confidence,which can improve the efficiency of the algorithm considerably.This method has been applied to Zhejiang Tobacco CRM system,and the results show that the improved algorithm is very effective.
出处
《电脑知识与技术》
2015年第1X期106-109,共4页
Computer Knowledge and Technology
关键词
客户关系管理
数据挖掘
客户关系图
频繁项集
custom relationship management
data mining
customer relational graph
frequent etem set
