Mining frequent sets using fuzzy multiple-level association rules

At present, most of the association rules algorithms are based on the Boolean attribute and single-level association rules mining. But data of the real world has various types, the multi-level and quantitative attributes are got more and more attention. And the most important step is to mine frequent sets. In this paper, we propose an algorithm that is called fuzzy multiple-level association (FMA) rules to mine frequent sets. It is based on the improved Eclat algorithm that is different to many researchers' proposed algorithms that used the Apriori algorithm. We analyze quantitative data's frequent sets by using the fuzzy theory, dividing the hierarchy of concept and softening the boundary of attributes' values and frequency. In this paper, we use the vertical-style data and the improved Eclat algorithm to describe the proposed method, we use this algorithm to analyze the data of Beijing logistics route. Experiments show that the algorithm has a good performance, it has better effectiveness and high efficiency. © 2018, Journal of Electronic Science and Technology.