Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. Carnein, M. & Trautmann, H. Business & Information Systems Engineering, 61(3):277–297, June, 2019. ![Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time.
@article{carnein_optimizing_2019,
title = {Optimizing {Data} {Stream} {Representation}: {An} {Extensive} {Survey} on {Stream} {Clustering} {Algorithms}},
volume = {61},
issn = {1867-0202},
shorttitle = {Optimizing {Data} {Stream} {Representation}},
url = {https://doi.org/10.1007/s12599-019-00576-5},
doi = {10.1007/s12599-019-00576-5},
abstract = {Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time.},
language = {en},
number = {3},
urldate = {2022-03-15},
journal = {Business \& Information Systems Engineering},
author = {Carnein, Matthias and Trautmann, Heike},
month = jun,
year = {2019},
pages = {277--297},
}
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