Dynamic Differential Evolution Algorithm for Clustering Temporal Data

Temporal data clustering is the process of grouping similar patterns in a dataset together when the patterns change with time. This change in patterns introduces the issue of loss of diversity in differential evolution algorithms. The lack of re-diversification of the population limits the exploration ability of differential evolution algorithms resulting in early convergence around stale solutions. This paper describes and evaluates three algorithms that were applied to a temporal data clustering problem, namely the standard data clustering DE, the reinitialising data clustering DE, and the data clustering DynDE.

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