An effective evaluation measure for clustering on evolving data streams. Kremer, H., Kranen, P., Jansen, T., Seidl, T., Bifet, A., Holmes, G., & Pfahringer, B. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, of KDD '11, pages 868–876, New York, NY, USA, August, 2011. Association for Computing Machinery. ![An effective evaluation measure for clustering on evolving data streams [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Due to the ever growing presence of data streams, there has been a considerable amount of research on stream mining algorithms. While many algorithms have been introduced that tackle the problem of clustering on evolving data streams, hardly any attention has been paid to appropriate evaluation measures. Measures developed for static scenarios, namely structural measures and ground-truth-based measures, cannot correctly reflect errors attributable to emerging, splitting, or moving clusters. These situations are inherent to the streaming context due to the dynamic changes in the data distribution. In this paper we develop a novel evaluation measure for stream clustering called Cluster Mapping Measure (CMM). CMM effectively indicates different types of errors by taking the important properties of evolving data streams into account. We show in extensive experiments on real and synthetic data that CMM is a robust measure for stream clustering evaluation.
@inproceedings{kremer_effective_2011,
address = {New York, NY, USA},
series = {{KDD} '11},
title = {An effective evaluation measure for clustering on evolving data streams},
isbn = {978-1-4503-0813-7},
url = {https://doi.org/10.1145/2020408.2020555},
doi = {10.1145/2020408.2020555},
abstract = {Due to the ever growing presence of data streams, there has been a considerable amount of research on stream mining algorithms. While many algorithms have been introduced that tackle the problem of clustering on evolving data streams, hardly any attention has been paid to appropriate evaluation measures. Measures developed for static scenarios, namely structural measures and ground-truth-based measures, cannot correctly reflect errors attributable to emerging, splitting, or moving clusters. These situations are inherent to the streaming context due to the dynamic changes in the data distribution. In this paper we develop a novel evaluation measure for stream clustering called Cluster Mapping Measure (CMM). CMM effectively indicates different types of errors by taking the important properties of evolving data streams into account. We show in extensive experiments on real and synthetic data that CMM is a robust measure for stream clustering evaluation.},
urldate = {2021-10-07},
booktitle = {Proceedings of the 17th {ACM} {SIGKDD} international conference on {Knowledge} discovery and data mining},
publisher = {Association for Computing Machinery},
author = {Kremer, Hardy and Kranen, Philipp and Jansen, Timm and Seidl, Thomas and Bifet, Albert and Holmes, Geoff and Pfahringer, Bernhard},
month = aug,
year = {2011},
keywords = {evaluation measure, stream clustering},
pages = {868--876},
}
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