  Clustering in dynamic environments is of increasing importance, with broad
applications ranging from real-time data analysis and online unsupervised
learning to dynamic facility location problems. While meta-heuristics have
shown promising effectiveness in static clustering tasks, their application for
tracking optimal clustering solutions or robust clustering over time in dynamic
environments remains largely underexplored. This is partly due to a lack of
dynamic datasets with diverse, controllable, and realistic dynamic
characteristics, hindering systematic performance evaluations of clustering
algorithms in various dynamic scenarios. This deficiency leads to a gap in our
understanding and capability to effectively design algorithms for clustering in
dynamic environments. To bridge this gap, this paper introduces the Dynamic
Dataset Generator (DDG). DDG features multiple dynamic Gaussian components
integrated with a range of heterogeneous, local, and global changes. These
changes vary in spatial and temporal severity, patterns, and domain of
influence, providing a comprehensive tool for simulating a wide range of
dynamic scenarios.
