Delineation of potential seismic sources using weighted K-means cluster analysis and particle swarm optimization (PSO)

Sheikhhosseini, Zohreh; Mirzaei, Noorbakhsh; Heidari, Reza; Monkaresi, Hamed

doi:10.1007/s11600-021-00683-6

Cited by 12 publications

(11 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 4 gives the chosen parameter set of the clustering algorithm for each dataset based on the analysis in Appendix C and a summary on the statistics of the detected clusters. In the CG area, we identified the largest number of seismic clusters (255) due to the increased detectability of micro-seismicity (low completeness magnitude threshold), however, they are short in size ( n = 18.28) and duration ( τ = 12.50) Conversely, the CII area is characterized by a small number of seismic clusters (45) but with large mean size ( n = 118.43) and duration ( τ = 54.60). The clustered seismicity is prevalent (75%), whereas in CG and NAS, the background component is more dominant than clustered seismicity with 64% and 56%, respectively (Table 4).…”

Section: Cluster Analysismentioning

confidence: 93%

“…It has been efficiently applied for detecting similarities among earthquake locations, origin times and focal mechanisms [34,44]. An advantage of the algorithm is that it does not require as input a predefined number of earthquake clusters, such as the k-means algorithm, where further optimization techniques for the determination of the clusters number are necessary [45].…”

Section: Dbscan Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Identification and Temporal Characteristics of Earthquake Clusters in Selected Areas in Greece

2022

View full text Add to dashboard Cite

The efficiency of earthquake clustering investigation is improved as we gain access to larger datasets due to the increase of earthquake detectability. We aim to demonstrate the robustness of a new clustering method, MAP-DBSCAN, and to present a comprehensive analysis of the clustering properties in three major seismic zones of Greece during 2012–2019. A time-dependent stochastic point model, the Markovian Arrival Process (MAP), is implemented for the detection of change-points in the seismicity rate and subsequently, a density-based clustering algorithm, DBSCAN, is used for grouping the events into spatiotemporal clusters. The two-step clustering procedure, MAP-DBSCAN, is compared with other existing methods (Gardner-Knopoff, Reasenberg, Nearest-Neighbor) on a simulated earthquake catalog and is proven highly competitive as in most cases outperforms the tested algorithms. Next, the earthquake clusters in the three areas are detected and the regional variability of their productivity rates is investigated based on the generic estimates of the Epidemic Type Aftershock Sequence (ETAS) model. The seismicity in the seismic zone of Corinth Gulf is characterized by low aftershock productivity and high background rates, indicating the dominance of swarm activity, whereas in Central Ionian Islands seismic zone where main shock-aftershock sequences dominate, the aftershock productivity rates are higher. The productivity in the seismic zone of North Aegean Sea vary significantly among clusters probably due to the co-existence of swarm activity and aftershock sequences. We believe that incorporating regional variations of the productivity into forecasting models, such as the ETAS model, it might improve operational earthquake forecasting.

show abstract

Section: Cluster Analysismentioning

confidence: 93%

Section: Dbscan Algorithmmentioning

confidence: 99%

Identification and Temporal Characteristics of Earthquake Clusters in Selected Areas in Greece

2022

View full text Add to dashboard Cite

show abstract

“…As one of the representative algorithms in the data mining field, the K-means clustering technique is very popular in certain areas of life [24]. e algorithm's core principle is to assume that clusters are made up of samples with similar distances, then repeatedly update the center of each cluster based on the position of the samples in the cluster until the total of the distances within the cluster is minimized.…”

Section: K-meansmentioning

confidence: 99%

Enterprise Precision Marketing Effectiveness Model Based on Data Mining Technology

Meng

2022

Mobile Information Systems

View full text Add to dashboard Cite

Due to the explosive growth of massive consumption data, the traditional marketing model of enterprises has become stretched. The rapid advancement of data mining technology has given new impetus to the innovation of the marketing strategies of enterprises, promoting the progressive transformation of enterprises from traditional passive marketing to precise and refined marketing. Data mining technology’s basic task is to analyze data, acquire insight into cause and effect, and then predict the future. As a result, the technology is coupled with the enterprises’ marketing activities, and a precise marketing model is built using big data consumption. This will aid in the comprehensive and three-dimensional description of customers, as well as the analysis of potential customers’ attributes, to give a scientific basis for the formulation and implementation of precise data-driven decision-making for enterprises. As a result, our study enhances and optimizes the K-means algorithm in combination with the artificial bee colony algorithm aiming at fixing the issue that the K-means algorithm is sensitive to cluster center initialization and improving the enterprise precision marketing model’s clustering performance. In the precise marketing scenario of the telecom business, the improved K-means clustering model is utilized to realize the analysis and prediction of telecom customers, as well as to carry out precise marketing based on the predicted findings. Finally, the optimized K-means clustering algorithm can objectively and comprehensively reflect the characteristics of telecom customer value segmentation, efficiently mining future clients and preventing blind marketing by enterprises, based on the model's actual verification results. Simultaneously, it provides substantial data support for telecom enterprises’ resource planning, as well as pointing out the next step in increasing market share.

show abstract

“…Enterprises in the cluster can also promote the formation of cooperative innovation network through the evolution of industry-university-research cooperative innovation, and the phenomenon of technology diffusion and knowledge spillover under this innovation mode can promote the spatial aggregation of enterprises. In the mode of industrial cluster, enterprise cooperative innovation between behavior is based on supply chain network and cluster supply chain network and needed a chain in the middle and lower reaches of the suppliers, manufacturers, distributors, and other support and also needed to merge the government agencies, research institutions, and financial institutions to provide support for cooperative innovation of industrial cluster [2]. Therefore, it can be seen that the evolution of industry-university-research cooperative innovation in enterprise clusters is not accomplished overnight and requires a lot of attention.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Game Analysis of Industry-University-Research Cooperative Innovation in Digital Media Enterprise Cluster Based on GS Algorithm

Duan

Sun

Wang

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In order to study all the advantages and disadvantages of digital media enterprises, technological innovation can only be completed through cooperation. A kind of industry-university-research cooperative innovation evolutionary game method based on GS algorithm is proposed for digital media enterprise clusters. This method analyzes the evolutionary game theory of innovation and puts forward the evolutionary stability strategy of cooperative innovation between enterprises and research institutions. The results show that decreasing V is beneficial for the evolutionary game to approach the equilibrium point (1,1); that is, the greater the cost of independent innovation is compared with collaborative innovation, the stronger the willingness of both sides of the game to collaborative innovation. Enterprises and scientific research institutions are two different subjects with different interests. If they want to complete innovation cooperation, they need to formulate a perfect set of rules so that both sides of the game can carry out cooperative innovation according to the principles, so as to achieve the goal of cooperation.

show abstract

Delineation of potential seismic sources using weighted K-means cluster analysis and particle swarm optimization (PSO)

Cited by 12 publications

References 47 publications

Identification and Temporal Characteristics of Earthquake Clusters in Selected Areas in Greece

Identification and Temporal Characteristics of Earthquake Clusters in Selected Areas in Greece

Enterprise Precision Marketing Effectiveness Model Based on Data Mining Technology

Evolutionary Game Analysis of Industry-University-Research Cooperative Innovation in Digital Media Enterprise Cluster Based on GS Algorithm

Contact Info

Product

Resources

About