Hydrological Time Series Motif Association Rule Mining Based on Three-Step Pruning and Constraints.

With the rapid development of big data technology, how to mine useful association rules from a large amount of time series data is an important task in the field of water resources. Mining potential association rules between time series modalities has become a hot topic, which will provide effective help for water governance to predict future hydrological trends. The traditional method of mining hydrological data has two drawbacks: Firstly, the traditional method focuses more on the similarity of modalities in the process of modal mining, so it is easy to mine similar modalities and instances in large-scale data, thus generating redundancy; secondly, the traditional method ignores asynchronous rules in the mining of temporal association rules, and this will lose many valuable rules. In this paper, we propose a three-step pruning method for modal mining, which improves on the MASS algorithm. We propose a constraint-based association rule mining algorithm that achieves asynchronous rule mining by introducing a minimum overlap threshold and a maximum time interval. Finally, we conducted experiments on the hydrological dataset. Compared with other methods, the experimental results show that the method proposed in this paper is effective.