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2010 IEEE International Conference on Data Mining Workshops
Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA
Sydney, Australia
December 13-December 13
ISBN: 978-0-7695-4257-7
| ASCII Text | x | ||
| Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer, "Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA," 2012 IEEE 12th International Conference on Data Mining Workshops, pp. 1400-1403, 2010 IEEE International Conference on Data Mining Workshops, 2010. | |||
| BibTex | x | ||
| @article{ 10.1109/ICDMW.2010.17, author = {Philipp Kranen and Hardy Kremer and Timm Jansen and Thomas Seidl and Albert Bifet and Geoff Holmes and Bernhard Pfahringer}, title = {Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA}, journal ={2012 IEEE 12th International Conference on Data Mining Workshops}, volume = {0}, year = {2010}, isbn = {978-0-7695-4257-7}, pages = {1400-1403}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDMW.2010.17}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 IEEE 12th International Conference on Data Mining Workshops TI - Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA SN - 978-0-7695-4257-7 SP1400 EP1403 A1 - Philipp Kranen, A1 - Hardy Kremer, A1 - Timm Jansen, A1 - Thomas Seidl, A1 - Albert Bifet, A1 - Geoff Holmes, A1 - Bernhard Pfahringer, PY - 2010 KW - data streams KW - clustering KW - evaluation measures VL - 0 JA - 2012 IEEE 12th International Conference on Data Mining Workshops ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2010.17
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 or the evaluation measures. In our demo, we present a novel experimental framework for both tasks. It offers the means for extensive evaluation and visualization and is an extension of the Massive Online Analysis (MOA) software environment released under the GNU GPL License.
Index Terms:
data streams, clustering, evaluation measures
Citation:
Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer, "Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA," icdmw, pp.1400-1403, 2010 IEEE International Conference on Data Mining Workshops, 2010
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