distributed hybrid renewable energy generation system for rural regions in Western Australia. The K-Means and K-Medoids clustering … for the region. The clustering algorithms were evaluated using the Dunn index, such that K-Means performed to a higher …

diversification, and how an acknowledgment of volatility clustering can enhance the quality of risk models. The analysis is carried …

their mesoregions. We applied a four-stage methodology, compared the results of two agglomerative clustering methods, and …

analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum … with the use of Big Data. Practical implications The use of Big Data in clustering applied to productivity analysis can … productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been …

, regression and clustering modelling. Sets of parameters were selected from secondary data via correlation and regression analyses …

develop a clustering-based approach to sustainable supplier segmentation. Design/methodology/approach - The characteristics of … determined using the best-worst method. Then, the K-means clustering algorithm was applied to all company suppliers based on four … results prove that supplier segmentation is more efficient when using clustering algorithms, and the best criteria are …

This study explores the stylized facts, volatility clustering, other highly irregular behaviour, and risk measures of … expect in the worst case scenario. Our results confirm the existence of predictability, volatility clustering, and …