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Clustering Spatiotemporal Data: An Augmented Fuzzy C-Means

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3 Author(s)
Hesam Izakian ; Department of Electrical and Computer Engineering, University of Alberta, Edmonton ; Witold Pedrycz ; Iqbal Jamal

In spatiotemporal data commonly encountered in geographical systems, biomedical signals, and the like, each datum is composed of features comprising a spatial component and a temporal part. Clustering of data of this nature poses challenges, especially in terms of a suitable treatment of the spatial and temporal components of the data. In this study, proceeding with the objective function-based clustering (such as, e.g., fuzzy C-means), we revisit and augment the algorithm to make it applicable to spatiotemporal data. An augmented distance function is discussed, and the resulting clustering algorithm is provided. Two optimization criteria, i.e., a reconstruction error and a prediction error, are introduced and used as a vehicle to optimize the performance of the clustering method. Experimental results obtained for synthetic and real-world data are reported.

Published in:

IEEE Transactions on Fuzzy Systems  (Volume:21 ,  Issue: 5 )