摘要
根据动态交易行为对供应商分类,更好地为供应商提供服务,是大型企业供应商关系管理的核心问题之一。针对供应商行为的交易数据最大、表达复杂的特点,提出基于k-均值子空间聚类算法对供应商分类的数据挖掘方法,解决高维和稀疏数据的分析问题,并通过实例验证该方法的准确性和高效性。结果表明该方法是优化供应商关系,提高企业能力的有效方法。
This paper defines that supplier categorization based on dynamic transaction behavior, in order to offer better service for suppliers, is one of core problems in supplier relationship management （SRM）. It proposes a K-means type subspace clustering for supplier categorization to analyze the high dimensional and sparse data, because transaction data of supplier behavior is mass and complicated. Therefore, this is a proper way to optimize supplier relations, and improve enterprises＇ competence. Finally, a real example is given to demonstrate the effectiveness and veracity of this method.
出处
《工业工程》
2007年第3期76-79,共4页
Industrial Engineering Journal
基金
国家自然科学基金资助项目(60475026)
关键词
供应商关系管理
供应商分类
数据挖掘
子空间聚类
supplier relationship management
supplier categorization
data mining
subspace clustering
