For each subset of events, we apply the EFCM algorithm to detect the final cluster prototypes.
The hyperspheres obtained as clusters (circles in case of two dimensions) by using EFCM can represent hotspots in hotspot analysis; this method has a linear computational complexity and is robust to noises and outliers.
In this paper, we present a new method that uses the EFCM algorithm for studying the spatio-temporal evolution of hotspots in disease analysis.
In other words, the EFCM algorithm can be summarized in the following steps.
In the EFCM algorithm, we consider clustering prototypes Given by hyper spheres in the n-dimensional feature's space.
In Section 2, we give an overview of the EFCM algorithm.
In this research, we present a method for studying the spatio-temporal evolution of hotspots areas in disease analysis; we apply the EFCM algorithm for comparing, in consecutive years, event datasets corresponding to oto-laryngopharyngeal diseases diagnosis detected in the district of Naples (I).
The cluster prototypes detected from EFCM method are circular areas on the map that can approximate a hotspot area.
EFCM 8.0 for OEMs inherits new features from SANavigator 4.0, including: multi-vendor capability., user defined groups and views; improved import/export; centralised device list; and greater scalability.
SANavigator 4.0 gains new features from EFCM 8.0, including: show route; MEDATA element manager launch; and FICON CUP server management., SANavigator 4.0 also supports Ciscos MDS product line.