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Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms

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  • Matthias Carnein

    (University of Münster)

  • Heike Trautmann

    (University of Münster)

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.

Suggested Citation

  • Matthias Carnein & Heike Trautmann, 2019. "Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 277-297, June.
    • Handle: RePEc:spr:binfse:v:61:y:2019:i:3:d:10.1007_s12599-019-00576-5
    DOI: 10.1007/s12599-019-00576-5
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    References listed on IDEAS

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    1. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
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    1. Mohamad Alissa & Kevin Sim & Emma Hart, 2023. "Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches," Journal of Heuristics, Springer, vol. 29(1), pages 1-38, February.
    2. Jia Ming Yeoh & Fabio Caraffini & Elmina Homapour & Valentino Santucci & Alfredo Milani, 2019. "A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation," Mathematics, MDPI, vol. 7(12), pages 1-24, December.

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