Methods for Multi-Objective Genetic Clustering of Time-Evolving Data [thesis]

Austin Alleman
2020 unpublished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,
more » ... , etc. In traditional evolutionary clustering contexts, for specific choices of minimization criterion, a routine based on approximation or relaxation optimizes a user-determined trade-off between two objective functions -one reflecting goodness-of-fit of a clustering arrangement against historical data or clusters, and one goodness-of-fit against the most current data. However, not much attention has been devoted to the use of a posteriori multi-objective optimization algorithms for simultaneous optimization of these competing objectives, which naturally accommodate multiple costs of complicated form and organically detect a range of solutions exhibiting differing emphasis on one or the other of historical and current costs without the need
doi:10.21985/n2-13xt-6t48 fatcat:qe3nt25s5rg3lcobvgyjn7nw4e