Fuzzy Clustering Algorithm Based on Tree for Association Rules

It is one of the problems in association rules mining that a great many of rules generated from the dataset makes it difficult to analyze and use. An algorithm named FCABTAR for association rules clustering is proposed and applied to association rules managing. Firstly, an example is presented to demonstrate the weakness by the distance clustering. Secondly, the definition of fuzzy simulation degree and simulated matrix for association rules are put forward. Thirdly, a new algorithm based on a dynamic tree is brought forward, which can be used to implement the fuzzy clustering. Experiment with the UCI dataset shows that this algorithm can efficiently cluster the association rules for a user to understand.