Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA

@article{Kranen2010ClusteringPO,
  title={Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA},
  author={P. Kranen and H. Kremer and T. Jansen and T. Seidl and A. Bifet and Geoff Holmes and B. Pfahringer},
  journal={2010 IEEE International Conference on Data Mining Workshops},
  year={2010},
  pages={1400-1403}
}
In today's applications, evolving data streams are ubiquitous. Stream clustering algorithms were introduced to gain useful knowledge from these streams in real-time. The quality of the obtained clusterings, i.e. how good they reflect the data, can be assessed by evaluation measures. A multitude of stream clustering algorithms and evaluation measures for clusterings were introduced in the literature, however, until now there is no general tool for a direct comparison of the different algorithms… Expand
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