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

• URL: http://arxiv.org/abs/2402.15731v2
• Date: Tue, 9 Apr 2024 09:40:22 GMT
• Title: Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes
• Authors: Danial Yazdani, Juergen Branke, Mohammad Sadegh Khorshidi, Mohammad Nabi Omidvar, Xiaodong Li, Amir H. Gandomi, Xin Yao,
• Abstract summary: 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. 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.
• Score: 11.56518009058007
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 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.

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