基于数据挖掘的供应链产品优化配置

Optimal Configurations of Supply Chain Products Based on Data Mining

针对供应链的特点,以客户偏好序列数据为切入点,提出客户偏好取向与客户特征属性间的关联关系模型,借鉴数据挖掘的符号序列聚类方法,研究符号类型序列数据对应的性质,从形式化和实例化2个方向讨论符号序列相似性问题,对偏好符号序列聚类问题的本质进行分析,研究如何应用自组织特征映射作为符号序列的聚类算法,并对聚类模型进行比较,使得从消费者偏好进行市场细分结构研究的研究途径在实际应用中得以实现。

Aiming at the characteristics of supply chain, considering the customer preferences sequence data, this paper presents an association relation model relating customer preferences and customer characteristic attributes. Referencing cluster method for the symbol sequence in data mining, corresponding properties of the data from type symbol sequence are studied. The research follows two directions, formal and instantiation, to discuss similarity issues of symbol sequence in which essential problems in cluster from preference symbol sequence clustering are analized. This paper studies on how to apply self-organizing feature map as a symbol of the sequence clustering algorithm, and compares clustering model, thus enabling from the consumer preference for market segmentation studies in the structure means in practical application is realized.