Abstract
Cluster analysis of data delivered in a stream exhibits some unique properties. They make the clustering more difficult than it happens for the static set of data. This paper introduces a new DeltaDens clustering algorithm that can be used for this purpose. It is a density–based algorithm, capable of finding an unbound number of irregular clusters. The algorithm’s per–iteration processing time linearly depends on the size of its internal buffer. The paper describes the algorithm and delivers some experimental results explaining its performance and accuracy.
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References
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proc. of the 29th Int. Conf. on Very Large Data Bases, vol. 29, pp. 81–92. VLDB Endowment (2003)
Cao, F., Ester, M., Qian, W., Zhou, A.: Density–Based Clustering over an Evolving Data Stream with Noise. In: Proc. of the Sixth SIAM Int. Conf. on Data Mining, pp. 328–339. SIAM (2006)
Chen, Y., Tu, L.: Density–based clustering for real-time stream data. In: Proc. of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM (2007)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press (1996)
Kranen, P., Assent, I., Baldauf, C., Seidl, T.: The ClusTree: indexing micro-clusters for anytime stream mining. Knowledge and Information Systems 29, 249–272 (2011)
Sander, J., Ester, M., Kriegel, H.-P., Xu, X.: Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. Data Mining and Knowledge Discovery 2, 169–194 (1998)
Wan, L., Ng, W.K., Dang, X.H., Yu, P.S., Zhang, K.: Density-based clustering of data streams at multiple resolutions. ACM Trans. Knowl. Discov. Data 3, 14:1–14:28 (2009)
MOA (Massive Online Analysis), software release (March 2012), http://moa.cs.waikato.ac.nz/downloads/
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Ziembiński, R.Z. (2013). DeltaDens – Incremental Algorithm for On–Line Density–Based Clustering. In: Pechenizkiy, M., Wojciechowski, M. (eds) New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32518-2_16
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DOI: https://doi.org/10.1007/978-3-642-32518-2_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32517-5
Online ISBN: 978-3-642-32518-2
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