Mining association rules require two pieces of data, the transaction and what was bought in that transaction.
挖掘关联规则需要两方面的数据，事务及该事务中所包含的信息。
Discovering cyclic generalized association rules from transaction databases can reveal the relationship of different levels of the taxonomies and display cyclic variations over time.
在事务数据库中的周期性一般关联规则可以揭示类的不同层次之间的关系和呈现周期性变化。
This paper is focused on the methods of the construction of spatial transaction database, which is a crucial ste Pin the spatial association rules mining.
将空间数据库转换成空间事务数据库是空间关联规则挖掘过程的关键步骤。
Discovering the frequent set of item sequences in a transaction database is one of the most important tasks in mining association rules.
最大频繁项目序列集的生成是影响关联规则挖掘的关键问题，传统的算法是通过对事务数据库的多次扫描实现的。
Aiming at a familiar and simple constraint that some items must or must not present in rules, a fast clipped-transaction-based constraint association-rule mining algorithm was put forward.
针对一类常见而简单的规则中有项或缺项的约束，提出了一种基于事务数据修剪的约束关联规则的快速挖掘算法。
This paper will be of relevance for the supermarket transaction data, WEKA use software algorithms to find frequent Apriori of the set, then find the association rules.
本文将关联分析用于超市交易数据，使用WEKA软件举行Apriori算法探寻频仍项集，进而找到关联规则。
This paper will be of relevance for the supermarket transaction data, WEKA use software algorithms to find frequent Apriori of the set, then find the association rules.
本文将关联分析用于超市交易数据，使用WEKA软件举行Apriori算法探寻频仍项集，进而找到关联规则。
应用推荐