Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes

arXiv:2402.15731v1 Announce Type: new
Abstract: 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 …

abstract analysis application applications arxiv benchmark clustering cs.lg cs.ne data data analysis dataset dataset generation dynamic environments facility framework heuristics importance location meta real-time solutions tasks time data tracking type unsupervised unsupervised learning