Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 21, Issue 5, Pages 855-868Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2012.2233479
Keywords
Fuzzy clustering; reconstruction and prediction criteria; spatiotemporal data; weather data
Funding
- Alberta Innovates-Technology Futures
- Alberta Advanced Education and Technology
- Natural Sciences and Engineering Research Council of Canada
- Canada Research Chair Program
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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.
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