Robust Fuzzy Association Rule Mining Based on Neighbors-Considered Clustering with Amended Membership Function

Clustering based association rule mining algorithms usually deal with data sets by clustering numerical transactions into boolean ones then using the boolean method. However, the numerical data will often change slightly, which may be caused by errors in the data acquisition process or disturbance in the environment, as a result, the obtained association rules will change greatly. Therefore, the uncertainty in the association rule mining should be considered. In this paper, an improved fuzzy clustering based robust association rule mining algorithm (RFARM) is proposed, where a regularization term is added into the objective function in which each point considers its own k-nearest neighbors to offset small disturbance and we also derive the necessary conditions for the convergence to the local minimum. Meanwhile, fuzzy clustering methods with constraints produce ripple parts in membership functions, which cannot be explained in association rule mining. In order to solve this, we design a variant algorithm (RFARM) that can perform and be understood better than the frequently-used methods. Experimental results have shown that the proposed methods are superior in the accuracy of association rules and the anti-noise capability.