Efficiently extracting frequent patterns from continuous uncertain data

Uncertain frequent pattern mining has been much discussed in recent decades. It is widely used in various fields and helps analysts to comprehend the deep meaning of collected data from the frequencies of items. In past studies, researchers have focused on discrete models. However, a discrete model only explains the presence of combinations of items without giving specific data intervals. To compensate for the drawbacks of discrete models, we focus on continuous uncertain data and improve a continuous uncertain frequent tree for the extraction of frequent patterns, notably time costs. Attribute overlapping usually causes the high time cost in the extraction phase. To avoid long branches in the tree, two approaches are proposed. The first approach is to name each attribute at given level with an uncertain frequent pattern. By using links and reshaping the uncertain frequency tree, the number of combinations decreases. The second approach is called uncertain frequent pattern map transforming. It uses a discrete transformation to decrease the time cost. In experiments, our two approaches were compared with different mainstream approaches. According to the results, our approaches not only cost less time to explore frequent patterns but also exhibited high accuracy for continuous uncertain data.