---
res:
  bibo_abstract:
  - Analysing streaming data has received considerable attention over the recent years.
    A key research area in this field is stream clustering which aims to recognize
    patterns in a possibly unbounded data stream of varying speed and structure. Over
    the past decades a multitude of new stream clustering algorithms have been proposed.
    However, to the best of our knowledge, no rigorous analysis and comparison of
    the different approaches has been performed. Our paper fills this gap and provides
    extensive experiments for a total of ten popular algorithms. We utilize a number
    of standard data sets of both, real and synthetic data and identify key weaknesses
    and strengths of the existing algorithms.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Matthias
      foaf_name: Carnein, Matthias
      foaf_surname: Carnein
  - foaf_Person:
      foaf_givenName: Dennis
      foaf_name: Assenmacher, Dennis
      foaf_surname: Assenmacher
  - foaf_Person:
      foaf_givenName: Heike
      foaf_name: Trautmann, Heike
      foaf_surname: Trautmann
      foaf_workInfoHomepage: http://www.librecat.org/personId=100740
    orcid: 0000-0002-9788-8282
  bibo_doi: 10.1145/3075564.3078887
  dct_date: 2017^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/978-1-4503-4487-6/17/05
  dct_language: eng
  dct_title: An Empirical Comparison of Stream Clustering Algorithms@
...