---
_id: '46351'
abstract:
- lang: eng
  text: Clustering is an important field in data mining that aims to reveal hidden
    patterns in data sets. It is widely popular in marketing or medical applications
    and used to identify groups of similar objects. Clustering possibly unbounded
    and evolving data streams is of particular interest due to the widespread deployment
    of large and fast data sources such as sensors. The vast majority of stream clustering
    algorithms employ a two-phase approach where the stream is first summarized in
    an online phase. Upon request, an offline phase reclusters the aggregations into
    the final clusters. In this setup, the online component will idle and wait for
    the next observation in times where the stream is slow. This paper proposes a
    new stream clustering algorithm called evoStream which performs evolutionary optimization
    in the idle times of the online phase to incrementally build and refine the final
    clusters. Since the online phase would idle otherwise, our approach does not reduce
    the processing speed while effectively removing the computational overhead of
    the offline phase. In extensive experiments on real data streams we show that
    the proposed algorithm allows to output clusters of high quality at any time within
    the stream without the need for additional computational resources.
author:
- first_name: Matthias
  full_name: Carnein, Matthias
  last_name: Carnein
- first_name: Heike
  full_name: Trautmann, Heike
  id: '100740'
  last_name: Trautmann
  orcid: 0000-0002-9788-8282
citation:
  ama: Carnein M, Trautmann H. evoStream — Evolutionary Stream Clustering Utilizing
    Idle Times. <i>Big Data Research</i>. 2018;14:101–111. doi:<a href="https://doi.org/10.1016/j.bdr.2018.05.005">10.1016/j.bdr.2018.05.005</a>
  apa: Carnein, M., &#38; Trautmann, H. (2018). evoStream — Evolutionary Stream Clustering
    Utilizing Idle Times. <i>Big Data Research</i>, <i>14</i>, 101–111. <a href="https://doi.org/10.1016/j.bdr.2018.05.005">https://doi.org/10.1016/j.bdr.2018.05.005</a>
  bibtex: '@article{Carnein_Trautmann_2018, title={evoStream — Evolutionary Stream
    Clustering Utilizing Idle Times}, volume={14}, DOI={<a href="https://doi.org/10.1016/j.bdr.2018.05.005">10.1016/j.bdr.2018.05.005</a>},
    journal={Big Data Research}, author={Carnein, Matthias and Trautmann, Heike},
    year={2018}, pages={101–111} }'
  chicago: 'Carnein, Matthias, and Heike Trautmann. “EvoStream — Evolutionary Stream
    Clustering Utilizing Idle Times.” <i>Big Data Research</i> 14 (2018): 101–111.
    <a href="https://doi.org/10.1016/j.bdr.2018.05.005">https://doi.org/10.1016/j.bdr.2018.05.005</a>.'
  ieee: 'M. Carnein and H. Trautmann, “evoStream — Evolutionary Stream Clustering
    Utilizing Idle Times,” <i>Big Data Research</i>, vol. 14, pp. 101–111, 2018, doi:
    <a href="https://doi.org/10.1016/j.bdr.2018.05.005">10.1016/j.bdr.2018.05.005</a>.'
  mla: Carnein, Matthias, and Heike Trautmann. “EvoStream — Evolutionary Stream Clustering
    Utilizing Idle Times.” <i>Big Data Research</i>, vol. 14, 2018, pp. 101–111, doi:<a
    href="https://doi.org/10.1016/j.bdr.2018.05.005">10.1016/j.bdr.2018.05.005</a>.
  short: M. Carnein, H. Trautmann, Big Data Research 14 (2018) 101–111.
date_created: 2023-08-04T07:55:33Z
date_updated: 2023-10-16T13:33:43Z
department:
- _id: '34'
- _id: '819'
doi: 10.1016/j.bdr.2018.05.005
intvolume: '        14'
language:
- iso: eng
page: 101–111
publication: Big Data Research
status: public
title: evoStream — Evolutionary Stream Clustering Utilizing Idle Times
type: journal_article
user_id: '15504'
volume: 14
year: '2018'
...