Mining and updating association rules based on fuzzy concept lattice

An algorithm for mining and updating association rules based on fuzzy concept lattice is proposed. When a new attribute is added into the fuzzy concept lattice, it is not necessary to calculate all the frequent nodes and association rules. According to the incremental construction algorithm of fuzzy concept lattice, it is only necessary to deal with the new nodes that have changed. Therefore, the amount of calculation is reduced. The existing incremental construction algorithm of precise concept lattices based on attributes is extended so that it can be applied to fuzzy concept lattices. The pruning technology is used to improve the construction algorithm. The steps for generating and updating association rules are added. According to the extended algorithm, the fuzzy concept lattice can be constructed, and the corresponding association rules can be generated and updated at the same time. The experimental results show that the algorithm proposed in this paper greatly reduces the computational workload, and shortens the running time.