Clustering Spatiotemporal Data: An Augmented Fuzzy C-Means
| Autor: | Hesam Izakian, Witold Pedrycz, Iqbal Jamal |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Clustering high-dimensional data
Fuzzy clustering business.industry Applied Mathematics Correlation clustering Pattern recognition computer.software_genre Determining the number of clusters in a data set Data stream clustering Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering CURE data clustering algorithm Canopy clustering algorithm Data mining Artificial intelligence Cluster analysis business computer Mathematics |
| Zdroj: | IEEE Transactions on Fuzzy Systems. 21:855-868 |
| ISSN: | 1941-0034 1063-6706 |
| DOI: | 10.1109/tfuzz.2012.2233479 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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