Data stream clustering: A survey

By Jonathan de Andrade Silva, Elaine Ribeiro de Faria, Rodrigo C. Barros, Eduardo R. Hruschka, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama. CSUR, 2013

571 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/de3ddfdb3c8c995cff057e010db6ac70e06ef5b5

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In Artificial Intelligence

Citations in the corpus

Online Machine Learning in Big Data Streams (2018, background): “ement 3: Online learning from big data has to be implemented in a distributed stream processing architecture. Several surveys for online machine learning in general [91, 29, 206, 83, 92] and subﬁelds [228, 220, 57, 165, 193, 131, 243, 7, 207, 99] have appeared recently. Our survey is different, ﬁrst”
Density‐based clustering (2019, background): “Data streams are challenging data sources for clustering algorithms since new data are usually produced at a very fast speed (processing is required to be done at a similar speed) while users want to keep track with the current patterns and the evolution of patterns over time (Silva et al. (2013) pr”
Clustering Data Streams Based on Shared Density between Micro-Clusters (2016, methodology): “C LUSTERING data streams [1], [2], [3], [4] has become an important technique for data and knowledge engineering.”
Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017, methodology): “From the end-user point of view, outliers are important and meaningful data points that are standing out from the usual populations (clusters) that can be found in data streams (Silva et al. 2013).”
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection (2018, background): “In practice, a small number of instances can be stored at any given time, as done in stream clustering [12].”
Clustering algorithms: A comparative approach (2016, methodology): “This requires the application of novel approaches to quickly process the entire volume of continuously incoming data [93], the detection of new clusters that are formed and the identification of outliers [94].”
A survey on data stream clustering and classification (2015, background): “[86] introduced a taxonomy to classify data stream clustering algorithms.”
A Survey on Ensemble Learning for Data Stream Classification (2017, methodology): “Previous works organize data stream learning in different categorizations according to which speciﬁc learning problem the work discusses [Gama et al. 2014; Silva et al. 2013; Gama 2010; Read et al. 2012].”
A Comprehensive Survey of Clustering Algorithms (2015, unknown): “For more detailed information about this kind of clustering algorithms, you can refer to [128–131], D-Stream [41,132].”
Learning under Concept Drift: A Review (2019, methodology): “In later work, the authors of [11], [45] extended this algorithm to handle recurrent concepts by computing and comparing current concept to previously stored concepts.”