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

作     者：Yazdani, Danial Branke, Juergen Khorshidi, Mohammad Sadegh Omidvar, Mohammad Nabi Li, Xiaodong Gandomi, Amir H. Yao, Xin 

作者机构：Faculty of Engineering & Information Technology University of Technology Sydney Australia Warwick Business school University of Warwick United Kingdom School of Computing Leeds University Business School University of Leeds United Kingdom RMIT University Australia Faculty of Engineering & Information Technology University of Technology Sydney Australia University Research and Innovation Center [EKIK Obuda University Hungary Department of Computing and Decision Sciences Lingnan University China and CERCIA School of Computer Science University of Birmingham United Kingdom 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Clustering algorithms 

摘      要：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 under-explored. 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. © 2024, CC BY-NC-SA.