一种基于增长模式的交易序列聚类算法

A Clustering Algorithm for Transaction Sequences Based on Growth Patterns

对交易序列进行各种挖掘分析能为商家制定营销策略提供量化依据.文中从销售量及变化趋势角度研究交易序列数据集的内在结构,定义了一种反映价格变化趋势的增长模式及其错位组合距离和角度向量距离两种相似性度量,在此基础上设计一个考虑时限约束的目标函数进行聚类研究.实验数据采用真实的商品交易序列集,结果表明,在时限约束的条件下,增长模式这种特征提取方式及其模式间的两种距离函数能较好地产生聚类结果,且这些聚类结果能得到较好地解释.

Mining and analysis of transaction sequences provide quantifiable schemes for decision makers to generate sales strategies. By studying the structure of transaction sequence sets according to the commodity sales amount and their variation trend, a kind of growth pattern is defined which reflects the variation trend of commodity price, as well as two methods of similarity measure, shifted window combined distance and angle vector distance, are defined. Based on those definitions, a clustering research is conducted by a goal function with time constraints. The experiments are conducted on the real commodity transaction sequence datasets. The results show that, combined with the growth patterns of two functions, it produces better clustering results under the condition of the time constraint, which could be well explained in practice.