基于多最小支持度的加权关联规则挖掘算法

针对交易数据库中数据项重要性不同的现象,提出了新的加权关联规则模型,并基于该模型设计了一个基于多最小支持度的加权关联规则挖掘新算法,允许用户设定多个最小支持度,为数据项设置不同的权重,从而解决数据项的重要性各不相同且出现频率不均匀的问题,发现更多有趣的规则.理论证明了相关性质,并实验验证了算法的有效性.

Algorithm of weighted association rules mining with multiple minimum supports

A new model of weighted association rule was presented in order to solve the problem that data item have not the same importance in datasets. Based on this model, a new algorithm of mining weighted association rules with multiple minimum supports was proposed. The algorithm allows the user to specify varied minimum supports and items weights to reflect the importance and frequency of each data item in datasets . The algorithm aims to deal with problem that items have different importance and varied frequency in transaction database and find more interesting rules which involve both frequent and rare items. The correlative properties of model and algorithm were given and the theories were proved. Finally, the algorithm was tested on the experimental data. Experiment results show that the new algorithm is effective for large databases.