Clustering Data Streams Based on Shared Density between Micro-Clusters

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Memory Efficient Experience Replay for Streaming Learning (2018, background): “Density-based algorithms cluster data that lie in highdensity regions of feature space together, while labeling points in low-density regions as outliers [8], [10], [15], [29], [47].”
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, methodology): “Traditionally, methods [144] such as distancebased [145], [146], [147], and distributional techniques [148] have been widely used for detecting irregularities in time series data.”
Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017, methodology): “ DSC_DBSTREAM (Hahsler and Bolaños 2016) implements an extension of the simple data stream clustering algorithm called tNN threshold nearest-neighbors (tNN) which was developed for package rEMM by Hahsler and Dunham (2015, 2010).”
Anomaly Detection in Time Series: A Comprehensive Evaluation (2022, methodology): “DBStream [55] LOF [22] DILOF [95] COF [130] GeckoFSM [118] IF-LOF [36] EIF [58]”