Mining time-interval univariate uncertain sequential patterns

In this study, we propose two algorithms to discover time-interval univariate uncertain (U2) -sequential patterns from a set of univariate uncertain (U2)-sequences. A U2-sequence is a sequence that contains transactions of univariate uncertain data, where each attribute in a transaction is associated with a quantitative interval and a probability density function indicating the possibility that each value exists in the interval. Many sources record U2-sequences, such as atmospheric pollution sensors and network monitoring systems. Mining sequential patterns from these U2-sequences is important for understanding the intrinsic characteristics of the U2-sequences. The proposed two algorithms are based on the candidate generate-and-test methodology and pattern growth methodology, respectively. We performed a series of experiments to evaluate them in terms of runtime and memory consumption. The experimental results show that different algorithms excel when applied to different conditions. In general, the algorithm based on the pattern growth methodology is the better choice.