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Big Data in Complex Systems pp 105–145Cite as

Stream Clustering Algorithms: A Primer

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Part of the book series: Studies in Big Data ((SBD,volume 9))

Abstract

Stream data has become ubiquitous due to advances in acquisition technology and pervades numerous applications. These massive data gathered as continuous flow, are often accompanied by dire need for real-time processing. One aspect of data streams deals with storage management and processing of continuous queries for aggregation. Another significant aspect pertains to discovery and understanding of hidden patterns to derive actionable knowledge using mining approaches. This chapter focuses on stream clustering and presents a primer of clustering algorithms in data stream environment.

Clustering of data streams has gained importance because of its ability to capture natural structures from unlabeled, non-stationary data. Single scan of data, bounded memory usage, and capturing data evolution are the key challenges during clustering of streaming data. We elaborate and compare the algorithms on the basis of these constraints. We also propose a taxonomy of algorithms based on the fundamental approaches used for clustering. For each approach, a systematic description of contemporary, well-known algorithms is presented. We place special emphasis on synopsis data structure used for consolidating characteristics of streaming data and feature it as an important issue in design of a stream clustering algorithms. We argue that a number of functional and operational characteristics (e.g. quality of clustering, handling of outliers, number of parameters etc.) of a clustering algorithm are influenced by the choice of synopsis. A summary of clustering features that are supported by different algorithms is given. Finally, research directions for improvement in the usability of stream clustering algorithms are suggested.

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Authors and Affiliations

  1. Department of Computer Science, Acharya Narendra Dev College, University of Delhi, Delhi, India

    Sharanjit Kaur

  2. Department of Computer Science, University of Delhi, Delhi, India

    Vasudha Bhatnagar

  3. Computer Science and Engineering Department, University of Texas, Arlington, TX, USA

    Sharma Chakravarthy

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  1. Sharanjit Kaur
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  2. Vasudha Bhatnagar
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  3. Sharma Chakravarthy
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Correspondence to Sharanjit Kaur .

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Editors and Affiliations

  1. Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  3. Faculty of Elec. Eng. & Comp. Sci., Department of Computer Science, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Vaclav Snasael

  4. Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  5. School of Information Technology, Deakin University, Victoria, Australia

    Jemal H. Abawajy

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Kaur, S., Bhatnagar, V., Chakravarthy, S. (2015). Stream Clustering Algorithms: A Primer. In: Hassanien, A., Azar, A., Snasael, V., Kacprzyk, J., Abawajy, J. (eds) Big Data in Complex Systems. Studies in Big Data, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-11056-1_4

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