挖掘周期性一般关联规则

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。这些信息对于识别在关联中的趋势和预测非常有用。由于数据噪声对发现周期性一般关联规则的巨大影响 ,文中用噪声比来抑制数据噪声对发现周期性一般关联规则的影响。同时根据对周期性与一般高频集之间关系的分析 ,利用周期裁剪技术来节省挖掘时间 ,给出了 (Cyclic general-ized itemsets,CGI)算法。实验证明 ,该算法可高效地发现周期性一般关联规则。

MINING CYCLIC GENERALIZED ASSOCIATION RULES

Discovering cyclic generalized association rules from transaction databases can reveal the relationship of different levels of the taxonomies and display cyclic variations over time. Information about such variations is great use of better identifying trends in associations and forecasting. Because cyclic rules are quite sensitive to a little noise, this paper uses the noise ratio as the criterion of identifying cyclic itemsets for dealing with the problem and utilizes the cycle pruning technique to reduce the computing time of the data mining process by exploiting the relationship between the cycle and generalized frequent itemsets. The paper gives the algorithm of mining cyclic generalized itemsets (CGI). Experiment shows that the CGI algorithm can efficiently yield results.