摘要
量化对象间相似性 /差别的方法具有广泛的用途 ,利用相关的语义信息能够得到更好的量化结果 提出了一个量化对象间语义差别的距离函数XDist,它基于线性优化中的运输问题模型和相关的语义信息量化两个对象之间的差别 在量化特征的差别函数是度量 (metric)的情况下 ,XDist是一个度量 ,在提高搜索的效率方面具有优势 ,弥补了以往研究的不足 ,而且实验初步表明 ,此函数在最近邻查询效果。
Quantifying similarity/difference between two objects plays an important role in many contexts The quality of the similarity/difference scores can be improved by considering the semantic information related to the features of objects A flexible semantic distance function called X Dist is proposed, which can utilize the semantic information to measure the difference between two objects based on a solution to the transportation problem from linear optimization With a ground distance function for single features being a metric, X Dist is also a metric This property is very useful for making searching efficient, but is not investigated in the previous research Moreover, the experimental results show X Dist can be as good as the previously studied similarity measures in nearest neighbor searching, discriminative power and computing speed
出处
《计算机研究与发展》
EI
CSCD
北大核心
2004年第10期1728-1736,共9页
Journal of Computer Research and Development
基金
国家"八六三"高技术研究发展计划基金项目 ( 2 0 0 1AA113 181)
关键词
语义距离
度量
协同过滤
数据挖掘
聚类
semantic distance
metric
collaborative filtering
data mining
clustering