We present a spatiotemporal clustering method, namely SEFCM, which is a generalization of the extended fuzzy C-Means (EFCM) method for detecting hotspots in spatial analysis. Each pattern is formed by three features: the geographical coordinates and the period in which a certain event is occurred. This method is applied to a spatial dataset (formed by earthquake epicenters occurred in Southern Italy since 2001 till to 2014) for prediction of the hotspots obtained in a given year. Comparisons of the prediction results are also made with the ones obtained by applying the known ST-DBSCAN algorithm.
Spatiotemporal extended fuzzy C-means clustering algorithm for hotspots detection and prediction / DI MARTINO, Ferdinando; Pedrycz, Witold; Sessa, Salvatore. - In: FUZZY SETS AND SYSTEMS. - ISSN 0165-0114. - (2017), pp. 1-18. [10.1016/j.fss.2017.11.011]
Spatiotemporal extended fuzzy C-means clustering algorithm for hotspots detection and prediction
Ferdinando Di Martino;Salvatore Sessa
2017
Abstract
We present a spatiotemporal clustering method, namely SEFCM, which is a generalization of the extended fuzzy C-Means (EFCM) method for detecting hotspots in spatial analysis. Each pattern is formed by three features: the geographical coordinates and the period in which a certain event is occurred. This method is applied to a spatial dataset (formed by earthquake epicenters occurred in Southern Italy since 2001 till to 2014) for prediction of the hotspots obtained in a given year. Comparisons of the prediction results are also made with the ones obtained by applying the known ST-DBSCAN algorithm.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
