_Applied_  
_sciences_  
_Identification and Temporal Characteristics of Earthquake_  
Clusters in Selected Areas in Greece  
[PERSON] 1,*(c), [PERSON] 1(c) and [PERSON] 2  
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are separated into background and clustered ones according to the estimated probabilities. Important clustering features can be inferred using the stochastic algorithm [16; 17], whereas it can be also used for declustering to optimize the background seismicity rate estimates [9; 18]. [PERSON] et al. [19] introduced an ETAS model with a time-dependent background component for the detection of aseismic transients. The modified ETAS model is used efficiently to reveal both main shock-aftershocks and earthquake swarms [20; 21]. Finally, the Nearest-Neighbor metric proposed by [PERSON] and [PERSON] [22] adopts a non-parametric definition of a cluster considering the space-time-magnitude proximity among earthquakes. [PERSON] and [PERSON] [23] introduced a binary threshold, \(\eta\), according to which the earthquakes are classified into clusters and background seismicity. They applied the algorithm on a global scale, revealing a link between the clustering properties of seismicity and the heat flow level [24]. The method is proven to be efficient in detecting main shock-aftershock sequences in Northeastern Italy [25], as well as earthquake repeaters in the Sea of Marmaras [26]. [PERSON] et al. [27] introduced a new approach to create probabilistic cluster networks based on the intersection between the background and clustered component of the nearest-neighbor distances. Another approach is based on the assumption of a common physical trigger during a seismic sequence, expressed by fluctuations in the occurrence rate [28; 29]. In our method, change-points of the intensity rate in the earthquake occurrences are detected with the use of the MAP model [30]. The temporal distribution of the events is approximated essentially by a non-homogeneous Poisson process, \(N_{t}\), with a piece-wise constant intensity rate determined by the underlying Markov process, \(J_{t}\). Simulation studies and applications on real datasets showed that the model efficiently identifies the changes in the earthquake occurrence rates [31]. Recent works by [PERSON] [32] and [PERSON] et al. [33] are based on non-stationary Poisson models whose rate is modulated by a hidden Markov process to determine a set of change-points for seismicity rate. Concerning earthquake clustering, [PERSON] et al. [34] proposed the combination of the MAP model with a density-based clustering algorithm, DBSCAN [35], for the detection of earthquake clusters in the spatiotemporal domain. They used the method on a micro-seismicity earthquake catalog in central Ionian Islands, Greece, efficiently revealing the clustered seismicity. Several studies suggest that the clustering properties of seismicity (spatiotemporal distribution, productivity rates) might be controlled by the tectonic regime.