[1]李 遥,荀亚玲.基于共享最近邻的客户交易数据聚类算法[J].计算机技术与发展,2022,32(01):73-78.[doi:10. 3969 / j. issn. 1673-629X. 2022. 01. 013]
　LI Yao,XUN Ya-ling.A Customer Transaction Data Clustering Algorithm Based onShared Nearest Neighbors[J].,2022,32(01):73-78.[doi:10. 3969 / j. issn. 1673-629X. 2022. 01. 013]
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基于共享最近邻的客户交易数据聚类算法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年01期
页码:
73-78
栏目:
大数据分析与挖掘
出版日期:
2022-01-10

文章信息/Info

Title:
A Customer Transaction Data Clustering Algorithm Based onShared Nearest Neighbors
文章编号:
1673-629X(2022)01-0073-06
作者:
李 遥; 荀亚玲
太原科技大学 计算机科学与技术学院,山西 太原 030024
Author(s):
LI Yao; XUN Ya-ling
School of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China
关键词:
聚类; 交易数据; 客户细分; 交易树; 共享最近邻
Keywords:
clustering; transaction data; customer segmentation; purchase tree; shared nearest neighbor
分类号:
TP301. 6
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 01. 013
摘要:
利用客户交易数据聚类分析,可得到更优异的客户细分效果,有助于企业更详实地了解消费者,制定精准的营销策略。 PurTreeClust 是一种新型的客户交易数据聚类算法,定义了一种新型的度量方式 PurTree 距离,可以很好地分析处理具有层次树结构的交易数据,但未考虑近邻点的影响,仅将交易树分配到距离最近的聚类中心所属类簇,容易出现错误的交易树分配。 该文利用交易树之间的共享最近邻信息,提出一种客户交易数据聚类算法。 该算法在聚类分配时,充分利用共享最近邻,首先分配类簇的从属交易树,然后分配类簇的可能从属交易树,实现聚类分配,可发现更加紧凑清晰的类簇,并避免了交易树错误分配,改善了客户细分效果。 最后采用 6 个真实客户交易数据集进行实验,验证了该算法的有效性。
Abstract:
By clustering analysis of customer transaction data,better customer segmentation effect can be obtained,which is helpful for enterprises to have a more detailed understanding of consumers and develop accurate marketing strategies. As a new clustering algorithm forcustomer transaction data,PurTreeClust defines a new measurement method,PurTree distance,which can analyze and process transactiondata with hierarchical tree structure. However,without considering the influence of neighboring points,only the purchase tree is allocatedto the class cluster belonging to the nearest cluster center,so the wrong purchase tree allocation is prone to occur. We propose a clusteringalgorithm for customer transaction data using the shared nearest neighbors information among purchase trees. The algorithm makes fulluse of the shared nearest neighbors to achieve cluster allocation. Firstly,the subordinate purchase tree of the cluster is allocated,and thenthe possible subordinate purchase tree of the cluster is allocated to realize cluster allocation. It can find more compact and clear clusters,avoid the wrong allocation of the purchase tree, and improve the effect of customer segmentation. Finally, experiments on six realcustomer transaction datasets verify that the proposed algorithm is more effective.

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更新日期/Last Update: 2022-01-10