IDEAS home Printed from https://ideas.repec.org/a/spr/binfse/v61y2019i3d10.1007_s12599-019-00576-5.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12599-019-00576-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12599-019-00576-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thiemo Fetzer & Samuel Marden, 2017. "Take What You Can: Property Rights, Contestability and Conflict," Economic Journal, Royal Economic Society, vol. 0(601), pages 757-783, May.
    2. Daniel Agness & Travis Baseler & Sylvain Chassang & Pascaline Dupas & Erik Snowberg, 2022. "Valuing the Time of the Self-Employed," Working Papers 2022-2, Princeton University. Economics Department..
    3. Orietta Nicolis & Jean Paul Maidana & Fabian Contreras & Danilo Leal, 2024. "Analyzing the Impact of COVID-19 on Economic Sustainability: A Clustering Approach," Sustainability, MDPI, vol. 16(4), pages 1-30, February.
    4. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    5. Yaeji Lim & Hee-Seok Oh & Ying Kuen Cheung, 2019. "Multiscale Clustering for Functional Data," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 368-391, July.
    6. Forzani, Liliana & Gieco, Antonella & Tolmasky, Carlos, 2017. "Likelihood ratio test for partial sphericity in high and ultra-high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 18-38.
    7. Yujia Li & Xiangrui Zeng & Chien‐Wei Lin & George C. Tseng, 2022. "Simultaneous estimation of cluster number and feature sparsity in high‐dimensional cluster analysis," Biometrics, The International Biometric Society, vol. 78(2), pages 574-585, June.
    8. Vojtech Blazek & Michal Petruzela & Tomas Vantuch & Zdenek Slanina & Stanislav Mišák & Wojciech Walendziuk, 2020. "The Estimation of the Influence of Household Appliances on the Power Quality in a Microgrid System," Energies, MDPI, vol. 13(17), pages 1-21, August.
    9. Caruso, Germán & Scartascini, Carlos & Tommasi, Mariano, 2015. "Are we all playing the same game? The economic effects of constitutions depend on the degree of institutionalization," European Journal of Political Economy, Elsevier, vol. 38(C), pages 212-228.
    10. Mehmet Çağlar & Cem Gürler, 2022. "Sustainable Development Goals: A cluster analysis of worldwide countries," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8593-8624, June.
    11. Jelle R Dalenberg & Luca Nanetti & Remco J Renken & René A de Wijk & Gert J ter Horst, 2014. "Dealing with Consumer Differences in Liking during Repeated Exposure to Food; Typical Dynamics in Rating Behavior," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    12. Daniel Lewis & Davide Melcangi & Laura Pilossoph, 2019. "Latent Heterogeneity in the Marginal Propensity to Consume," 2019 Meeting Papers 519, Society for Economic Dynamics.
    13. Chun-Xia Zhang & Jiang-She Zhang & Sang-Woon Kim, 2016. "PBoostGA: pseudo-boosting genetic algorithm for variable ranking and selection," Computational Statistics, Springer, vol. 31(4), pages 1237-1262, December.
    14. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
    15. Germán Caruso & Walter Sosa-Escudero & Marcela Svarc, 2015. "Deprivation and the Dimensionality of Welfare: A Variable-Selection Cluster-Analysis Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(4), pages 702-722, December.
    16. Fang, Yixin & Wang, Junhui, 2011. "Penalized cluster analysis with applications to family data," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2128-2136, June.
    17. Yingqiu Zhu & Qiong Deng & Danyang Huang & Bingyi Jing & Bo Zhang, 2021. "Clustering based on Kolmogorov–Smirnov statistic with application to bank card transaction data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 558-578, June.
    18. Aswin Balasubramanian & Floran Martin & Md Masum Billah & Osaruyi Osemwinyen & Anouar Belahcen, 2021. "Application of Surrogate Optimization Routine with Clustering Technique for Optimal Design of an Induction Motor," Energies, MDPI, vol. 14(16), pages 1-19, August.
    19. Youmi Suk, 2024. "A Within-Group Approach to Ensemble Machine Learning Methods for Causal Inference in Multilevel Studies," Journal of Educational and Behavioral Statistics, , vol. 49(1), pages 61-91, February.
    20. Henner Gimpel & Daniel Rau & Maximilian Röglinger, 2018. "Understanding FinTech start-ups – a taxonomy of consumer-oriented service offerings," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 245-264, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:binfse:v:61:y:2019:i:3:d:10.1007_s12599-019-00576-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.