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Methods for Multi-Objective Genetic Clustering of Time-Evolving Data
[thesis]
2020
unpublished
Sequential batches of time-evolving data for a set of persistent identifiable entities (e.g. online shopping behavior by month for a customer ID, or economic figures by year for a collection of countries) can exhibit temporal shifts in their underlying clustering structure. Methods for recovering this evolutionary clustering structure exploit natural smoothness in cluster evolution at consecutive time steps to stabilize cluster assignments as batches of updated data arrive daily, weekly,
doi:10.21985/n2-13xt-6t48
fatcat:qe3nt25s5rg3lcobvgyjn7nw4e