Abstract
In this paper we present a comparative study of three data stream clustering algorithms: STREAM, CluStream and MR-Stream. We used a total of 90 synthetic data sets generated from spatial point processes following Gaussian distributions or Mixtures of Gaussians. The algorithms were executed in three main scenarios: 1) low dimensional; 2) low dimensional with concept drift and 3) high dimensional with concept drift. In general, CluStream outperformed the other algorithms in terms of clustering quality at a higher execution time cost. Our results are analyzed with the non-parametric Friedman test and post-hoc Nemenyi test, both with α = 5%. Recommendations and future research directions are also explored.
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References
Aggarwal, C.C.: Data Streams: Models and Algorithms (Advances in Database Systems). Springer, Secaucus (2006)
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: VLDB 2003: Proceedings of the 29th International Conference on Very Large Data Bases. pp. 81–92. VLDB Endowment (2003)
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for projected clustering of high dimensional data streams. In: VLDB 2004: Proceedings of the Thirtieth International Conference on Very Large Data Bases. pp. 852–863. VLDB Endowment (2004)
Cao, F.: Density-based clustering over an evolving data stream with noise. In: Proc. Sixth SIAM Intl Conf. Data Mining (2006)
Chen, Y., Tu, L.: Density-based clustering for real-time stream data. In: KDD 2007: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM Press, New York (2007)
Csernel, B., Clerot, F., Hébrail, G.: Streamsamp – datastream clustering over tilted windows through sampling. In: Proceedings of the International Workshop on Knowledge Discovery from Data Streams, IWKDDS 2006 (2006)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. KDD, vol. 96, pp. 226–231 (1996)
Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining data streams: a review. SIGMOD Rec. 34(2), 18–26 (2005)
Gantz, J., Reinsel, D., Chute, C., Schlichting, W., McArthur, J., Minton, S., Xheneti, I., Toncheva, A., Manfrediz, A.: The expanding digital universe: A forecast of worldwide information growth through 2010. Tech. rep., IDC (2007)
Han, J., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann Publishers Inc., San Francisco (2000)
Hubert, L., Arabie, P.: Comparing partitions. Journal of Classification 2, 193–218 (1985), doi:10.1007/BF01908075
Jain, A.K.: Data clustering: 50 years beyond K-means. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 3–4. Springer, Heidelberg (2008)
Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River (1988)
Jain, K., Vazirani, V.V.: Primal-dual approximation algorithms for metric facility location and k-median problems. In: FOCS 1999: Proceedings of the 40th Annual Symposium on Foundations of Computer Science, p. 2. IEEE Computer Society Press, Washington, DC, USA (1999)
Kaufman, L., Rousseeuw, P.: Finding groups in data; an introduction to cluster analysis. Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics Section. EUA (1990)
Manning, C.D., Raghavan, P.: Schutze: Introduction to Information Retrieval, 1st edn. Cambridge University Press, Cambridge (2008)
O’Callaghan, L., Meyerson, A., Motwani, R., Mishra, N., Guha, S.: Streaming-data algorithms for high-quality clustering. In: Data Engineering, International Conference on, vol. 0, p. 0685 (2002)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley Longman Publishing Co., Boston (2005)
Udommanetanakit, K., Rakthanmanon, T., Waiyamai, K.: E-stream: Evolution-based technique for stream clustering. In: Alhajj, R., Gao, H., Li, X., Li, J., Zaïane, O.R. (eds.) ADMA 2007. LNCS (LNAI), vol. 4632, pp. 605–615. Springer, Heidelberg (2007)
Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: is a correction for chance necessary? In: ICML 2009, pp. 1073–1080. ACM Press, New York (2009)
Walpole, R., Myers, R., Myers, S., Ye, K.: Probability and statistics for engineers and scientists. Prentice-Hall, Upper Saddle River (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(3), 1–28 (2009)
Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. SIGMOD Rec. 25(2), 103–114 (1996)
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Pereira, C.M.M., de Mello, R.F. (2011). A Comparison of Clustering Algorithms for Data Streams. In: Hruschka, E.R., Watada, J., do Carmo Nicoletti, M. (eds) Integrated Computing Technology. INTECH 2011. Communications in Computer and Information Science, vol 165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22247-4_6
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DOI: https://doi.org/10.1007/978-3-642-22247-4_6
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