In recent years, many data mining methods have been proposed for finding useful and structured information from market basket data. The association rule model was recently proposed in order to discover useful patterns and dependencies in such data. This paper discusses a method for indexing market basket data efficiently for similarity search. The technique is likely to be very useful in applications which utilize the similarity in customer buying behavior in order to make peer recommendations. We propose an index called the
机译:有效的相似度搜索市场数据
机译:高维多媒体数据库中相似搜索的分层位图索引方法
机译:SPY-TECf一种高效的索引方法,用于在高维数据空间中进行相似性搜索
机译:市场篮下数据的相似性索引一种新方法
机译:时间序列和多类识别的相似性度量和索引方法。
机译:HBase中大空间数据相似性查询的基于象限的最小边界矩形树索引方法
机译:市场篮子数据相似性索引的新方法
机译:基于相似度的图像数据库索引效率的交易效率