Abstract
Many applications require the clustering of high-dimensional data streams. We propose a subspace clustering algorithm that can find clusters in different subspaces through one pass over a data stream. The algorithm combines the bottom-up grid-based method and top-down grid-based method. A uniformly partitioned grid data structure is used to summarize the data stream online. The top-down grid partition method is used o find the subspaces in which clusters locate. The errors made by the top-down partition procedure are eliminated by a mergence step in our algorithm. Our performance study with real datasets and synthetic dataset demonstrates the efficiency and effectiveness of our proposed algorithm.
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References
Henzinger, M.R., et al.: Computing on data streams. SRC Technical Note 1998-011, Digital systems research center, Palo Alto, California (1998)
O’Callaghan, L., et al.: Streaming-Data Algorithms for High-Quality Clustering. In: Proc. of the 18th International Conference on Data Engineering, pp. 685–694 (2002)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)
Aggarwal, C.C., et al.: A Framework for Clustering Evolving Data Streams. In: Proc. of the 29th VLDB Conference, pp. 81–92 (2003)
Aggarwal, C.C., et al.: A Framework for Projected Clustering of High Dimensional Data Streams. In: Proc. of the 30th VLDB Conference, pp. 852–863 (2004)
Park, N.H., Lee, W.S.: Statistical Grid-Based Clustering over Data Streams. ACM SIGMOD Record 33(1), 32–37 (2004)
Lu, Y., et al.: A Grid-Based Clustering Algorithm for High-Dimensional Data Streams. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS, vol. 3584, pp. 824–831. Springer, Heidelberg (2005)
Agrawal, R., et al.: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In: Proc. ACM SIGMOD Int. Conf. On Management of Data (SIGMOD 1998), pp. 94–105 (1998)
Goil, S., et al.: MAFIA: Efficient and Scalable Subspace Clustering for Very Large Data Sets. Technical Report, No. CPDC-TR-9906-010, Center for Parallel and Distributed Computing, Department of Electrical & Computer Engineering, Northwestern University (1999)
Hinneburg, A., Keim, D.A.: Optimal Grid-Clustring: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering. In: Proc. of the 25th VLDB Conference, pp. 506–517 (1999)
Baumgartner, C., et al.: Subspace Selection for Clustering High-Dimensional Data. In: Perner, P. (ed.) ICDM 2004. LNCS (LNAI), vol. 3275, pp. 11–18. Springer, Heidelberg (2004)
Sheikholeslami, G., Chatterjee, S., Zhang, A.: WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In: Proc. of the 24th VLDB Conference, pp. 428–439 (1998)
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Sun, Y., Lu, Y. (2006). A Grid-Based Subspace Clustering Algorithm for High-Dimensional Data Streams. In: Feng, L., Wang, G., Zeng, C., Huang, R. (eds) Web Information Systems – WISE 2006 Workshops. WISE 2006. Lecture Notes in Computer Science, vol 4256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11906070_4
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DOI: https://doi.org/10.1007/11906070_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47663-4
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