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Data stream clustering is a critical operation in various real-worldapplications, ranging from the Internet of Things (IoT) to social media andfinancial systems. Existing data stream clustering algorithms, while effectiveto varying extents, often lack the flexibility and self-optimizationcapabilities needed to adapt to diverse workload characteristics such asoutlier, cluster evolution and changing dimensions in data points. Theselimitations manifest in suboptimal clustering accuracy and computationalinefficiency. In this paper, we introduce MOStream, a modular andself-optimizing data stream clustering algorithm designed to dynamicallybalance clustering accuracy and computational efficiency at runtime. MOStreamdistinguishes itself by its adaptivity, clearly demarcating four pivotal designdimensions: the summarizing data structure, the window model for handling datatemporality, the outlier detection mechanism, and the refinement strategy forimproving cluster quality. This clear separation facilitates flexibleadaptation to varying design choices and enhances its adaptability to a widearray of application contexts. We conduct a rigorous performance evaluation ofMOStream, employing diverse configurations and benchmarking it against 9representative data stream clustering algorithms on 4 real-world datasets and 3synthetic datasets. Our empirical results demonstrate that MOStreamconsistently surpasses competing algorithms in terms of clustering accuracy,processing throughput, and adaptability to varying data stream characteristics.

MOStream: A Modular and Self-Optimizing Data Stream Clustering Algorithm