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Fuzzy C-Means with Spatiotemporal Constraints

Cluster analysis is a method of dividing the data into meaningful clusters based on the similarity of the data, making it possible to observe the data on a higher-level. Spatiotemporal data mining is a hot research topic in recent years. Currently, the research of spatiotemporal clustering analysis is still at a preliminary stage. Different from the existing methods for clustering spatiotemporal data, in this article we propose a novel fuzzy clustering method, fuzzy c-means with spatiotemporal constraints, which has both spatial and temporal constraints, considering closeness degree in both space and time. Using the Lagrange multiplier method, the iterative formulas of cluster centers and partition matrix are derived. The proposed method is verified using both artificially generated data and real data. Experimental results show that the algorithm can effectively treat trajectory data. For spatiotemporal data with transition points or mixed, the algorithm also has good applicability.