Abstract
Data stream clustering with sliding windows generates clusters for every window movement. Because repeated clustering on all changed windows is highly inefficient in terms of memory and computation time, a clustering algorithm should be designed with considering only inserted and deleted tuples of windows. In this paper, we address this problem by sliding window aggregation technique and cluster modification strategy. We propose a novel data structure for construction and maintenance of 2-level synopses. This data structure enables to update synopses efficiently and support precise sliding window operations. We also suggest a modification strategy to decide whether to append new synopses to pre-existing clusters or perform clustering on whole synopses according to the difference between probability distributions of the original and updated clusters. Experimental results show that proposed method outperforms state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ackermann, M.R., Märtens, M., Raupach, C., Swierkot, K., Lammersen, C., Sohler, C.: Streamkm++: a clustering algorithm for data streams. J. Exp. Algorithmics 17, 2.4:2.1–2.4:2.30 (2012)
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases, vol. 29, pp. 81–92 (2003). VLDB Endowment
Arasu, A., Babu, S., Widom, J.: The CQL continuous query language: semantic foundations and query execution. VLDB J. 15(2), 121–142 (2006)
Babcock, B., Datar, M., Motwani, R., O’Callaghan, L.: Maintaining variance and k-medians over data stream windows. In: Proceedings of the Twenty-Second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 234–243. ACM (2003)
Braverman, V., Lang, H., Levin, K., Monemizadeh, M.: Clustering problems on sliding windows. In: Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1374–1390. Society for Industrial and Applied Mathematics (2016)
Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: 2006 SIAM Conference on Data Mining, pp. 328–339. SIAM (2006)
Dang, X.H., Lee, V., Ng, W.K., Ciptadi, A., Ong, K.L.: An EM-based algorithm for clustering data streams in sliding windows. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds.) DASFAA 2009. LNCS, vol. 5463, pp. 230–235. Springer, Heidelberg (2009). doi:10.1007/978-3-642-00887-0_18
Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262. ACM (2004)
Guha, S., Meyerson, A., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams: theory and practice. IEEE Trans. Knowl. Data Eng. 15(3), 515–528 (2003)
Kranen, P., Assent, I., Baldauf, C., Seidl, T.: The clustree: indexing micro-clusters for anytime stream mining. Knowl. Inf. Syst. 29(2), 249–272 (2011)
Li, J., Maier, D., Tufte, K., Papadimos, V., Tucker, P.A.: No pane, no gain: efficient evaluation of sliding-window aggregates over data streams. SIGMOD Rec. 34(1), 39–44 (2005)
Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)
Silva, J.A., Faria, E.R., Barros, R.C., Hruschka, E.R., de Carvalho André, C.P.L.F., Gama, J.: Data stream clustering: a survey. ACM Comput. Surv. 46(1), 13:1–13:31 (2013)
Sun, L., Guo, C.: Incremental affinity propagation clustering based on message passing. IEEE Trans. Knowl. Data Eng. 26(11), 2731–2744 (2014)
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), 14:1–14:28 (2009)
Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. SIGMOD Rec. 25(2), 103–114 (1996)
Zhang, X., Furtlehner, C., Germain-Renaud, C., Sebag, M.: Data stream clustering with affinity propagation. IEEE Trans. Knowl. Data Eng. 26(7), 1644–1656 (2014)
Zhou, A., Cao, F., Qian, W., Jin, C.: Tracking clusters in evolving data streams over sliding windows. Knowl. Inf. Syst. 15(2), 181–214 (2008)
Acknowledgments
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-R0992-16-1023) supervised by the IITP(Institute for Information & communications Technology Promotion).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Youn, J., Choi, J., Shim, J., Lee, Sg. (2017). Partition-Based Clustering with Sliding Windows for Data Streams. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-55699-4_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55698-7
Online ISBN: 978-3-319-55699-4
eBook Packages: Computer ScienceComputer Science (R0)