The ClusTree: indexing micro-clusters for anytime stream mining

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Memory Efficient Experience Replay for Streaming Learning (2018, neutral): “global clustering (macro-clusters) [3], [5], [22], [45], [67], [75].”
Data stream clustering: A survey (2013, methodology): “In the .rst case, the clustering process is affected by the age of objects, which is explicitly modeled by a decay function [Aggarwal et al. 2003; Cao et al. 2006; Chen and Tu 2007; Kranen et al. 2011], as previously mentioned in Sections 3.2 and 3.3.”
Clustering Data Streams Based on Shared Density between Micro-Clusters (2016, background): “Over the last 10 years many algorithms for clustering data streams have been proposed [5], [6], [8], [9], [14], [15], [16], [17], [18], [19], [20].”
A survey on data stream clustering and classification (2015, methodology): “ClusTree uses an R*-tree structure [15] to index microclusters at different levels of granularities [60].”
Online Machine Learning in Big Data Streams (2018, methodology): “Another set of results describes the data stream implementation of DBSCAN [41, 55, 143].”
Online Learning: A Comprehensive Survey (2018, methodology): “Recent hierarchical clustering algorithms include the ClusTree (Kranen et al., 2011), which offers a compact self adapting index structure for storing stream summaries in addition to giving more importance to recent data, and Perch (Kobren et al.”