An Incremental Data Stream Clustering Algorithm Based on Dense Units Detection. Gao, J., Li, J., Zhang, Z., & Tan, P. In Ho, T. B., Cheung, D., & Liu, H., editors, Advances in Knowledge Discovery and Data Mining, of Lecture Notes in Computer Science, pages 420–425, Berlin, Heidelberg, 2005. Springer. doi abstract bibtex The data stream model of computation is often used for analyzing huge volumes of continuously arriving data. In this paper, we present a novel algorithm called DUCstream for clustering data streams. Our work is motivated by the needs to develop a single-pass algorithm that is capable of detecting evolving clusters, and yet requires little memory and computation time. To that end, we propose an incremental clustering method based on dense units detection. Evolving clusters are identified on the basis of the dense units, which contain relatively large number of points. For efficiency reasons, a bitwise dense unit representation is introduced. Our experimental results demonstrate DUCstream’s efficiency and efficacy.
@inproceedings{gao_incremental_2005,
address = {Berlin, Heidelberg},
series = {Lecture {Notes} in {Computer} {Science}},
title = {An {Incremental} {Data} {Stream} {Clustering} {Algorithm} {Based} on {Dense} {Units} {Detection}},
isbn = {978-3-540-31935-1},
doi = {10.1007/11430919_49},
abstract = {The data stream model of computation is often used for analyzing huge volumes of continuously arriving data. In this paper, we present a novel algorithm called DUCstream for clustering data streams. Our work is motivated by the needs to develop a single-pass algorithm that is capable of detecting evolving clusters, and yet requires little memory and computation time. To that end, we propose an incremental clustering method based on dense units detection. Evolving clusters are identified on the basis of the dense units, which contain relatively large number of points. For efficiency reasons, a bitwise dense unit representation is introduced. Our experimental results demonstrate DUCstream’s efficiency and efficacy.},
language = {en},
booktitle = {Advances in {Knowledge} {Discovery} and {Data} {Mining}},
publisher = {Springer},
author = {Gao, Jing and Li, Jianzhong and Zhang, Zhaogong and Tan, Pang-Ning},
editor = {Ho, Tu Bao and Cheung, David and Liu, Huan},
year = {2005},
pages = {420--425},
}
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