On the discovery of spatial-temporal fluctuating patterns

In this paper, we explore a new mining paradigm, called spatial-temporal fluctuating patterns (abbreviated as STFs), to discover potentially fluctuating and useful feature sets from the spatial-temporal data. These feature sets have some properties which are variant as time advances. Once STFs are discovered, we can find the turning points of patterns, which enables anomaly detection and transformation discovery over time. For example, the discovery of STFs can possibly figure out the phenomenon of virus variation during the epidemic outbreak, further providing the government with clues for the epidemic control. Therefore, we develop a union-based mining with the downward-closure structure to speed up the spatial-temporal mining process and dynamically compute fluctuating patterns. As shown in our experimental studies, the proposed framework can efficiently discover STFs on a real epidemic disease dataset, showing its prominent advantages to be utilized in real applications.