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• W4386319557 endingPage "119605" @default.
• W4386319557 startingPage "119605" @default.
• W4386319557 abstract "The number of partitions identified in a cluster analysis is traditionally a critical point of the procedure. There are many solutions available in the literature that researchers can exploit to guide how they determine the number of clusters. However, when a statistical analysis requires repeated cluster analyses, such as when tracking the changing composition of clusters over time, an automated approach can be beneficial. We propose a method to automatically cut dendrograms generated by a hierarchical clustering technique using a novel algorihtm called Model-Based Recursive Partitioning. As a case study, the method is applied to dynamically analyse the interdependencies between industry sectors during the pandemic period." @default.
• W4386319557 created "2023-09-01" @default.
• W4386319557 creator A5051544275 @default.
• W4386319557 creator A5087995350 @default.
• W4386319557 date "2023-11-01" @default.
• W4386319557 modified "2023-09-27" @default.
• W4386319557 title "Dynamic time series clustering with multivariate linkage and automatic dendrogram cutting using a recursive partitioning algorithm" @default.
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• W4386319557 doi "https://doi.org/10.1016/j.ins.2023.119605" @default.
• W4386319557 hasPublicationYear "2023" @default.
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