Abstract
The main aim of data stream subspace clustering is to find clusters in subspace in rational time accurately. The existing data stream subspace clustering algorithms are greatly influenced by parameters. Due to the flaws of traditional data stream subspace clustering algorithms, we propose SCRP, a new data stream subspace clustering algorithm. SCRP has the advantages of fast clustering and being insensitive to outliers. When data stream changes, the changes will be recorded by the data structure named Region-tree, and the corresponding statistics information will be updated. Further SCRP can regulate clustering results in time when data stream changes. According to the experiments on real datasets and synthetic datasets, SCRP is superior to the existing data stream subspace clustering algorithms on both clustering precision and clustering speed, and it has good scalability to the number of clusters and dimensions.
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References
Ling, C., Lingjun, Z., Li, T.: A clustering algorithm for multiple data streams based on spectral component similarity. Information Sciences 183(1), 35–47 (2012)
Weiguo, L., Jia, O.: Clustering algorithm for high dimensional data stream over sliding windows. In: Proc of the 10th Int. Conf. on Trust, Security and Privacy in Computing and Communications, pp. 1537–1542. IEEE, Piscataway (2011)
Halkidi, M., Koutsopoulos, I.: Online Clustering of distributed streaming data using belief propagation techniques. In: Proc of the 12th Int. Conf. on Mobile Data Management, pp. 216–225. IEEE, Piscataway (2011)
Aggarwal, C., Han, J., Wang, J., et al.: A framework for clustering evolving data streams. In: Proc of the 29th Int. Conf. on VLDB, pp. 81–92. Morgan Kaufmann (2003)
Parsons, L., Haque, E., Huan, L.: Subspace clustering for high dimensional data: A review. ACM SIGKDD Explorations Newsletter 6(1), 90–105 (2004)
Yihong, L., Yan, H.: Mining data streams using clustering. In: Proc of the 4th Int. Conf. on Machine Learning and Cybernetics, pp. 2079–2083. IEEE, Piscataway (2083)
Yufen, S.: Research on clustering algorithm based on grid. Huazhong University of Science and Technology, Wuhan (2006)
Park, N.H., Lee, W.S.: Cell tree: An adaptive synopsis structure for clustering multi-dimensional on-line data stream. Data & Knowledge Engineering 4(3), 1–22 (2007)
Park, N.H., Lee, W.S.: Grid-based subspace clustering over data streams. In: Proc of the ACM Conf. on Information and Knowledge Management, pp. 801–810. ACM, New York (2007)
Yanwei, Y., Qin, W., Jun, K., et al.: An on-line density-based clustering algorithm for spatial data stream. Acta Automatica Sinica 38(6), 1051–1059 (2012)
Dutta, B.R., Angelov, P.: Evolving local means method for clustering of streaming data. In: Proc of the 2012 Int. Conf. on World Congress on Computational Intelligence, pp. 1–8. IEEE, Piscataway (2012)
Lingjuan, L., Xiong, L.: An improved online stream data clustering algorithm. In: Proc of the 2nd Int. Conf. on Business Computing and Global Informatization, pp. 526–529. IEEE, Piscataway (2012)
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Yu, X., Xu, X., Lin, L. (2015). A Data Stream Subspace Clustering Algorithm. In: Wang, H., et al. Intelligent Computation in Big Data Era. ICYCSEE 2015. Communications in Computer and Information Science, vol 503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46248-5_41
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DOI: https://doi.org/10.1007/978-3-662-46248-5_41
Publisher Name: Springer, Berlin, Heidelberg
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