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Dynamic Stream Clustering Using Ants

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Advances in Computational Intelligence Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 513))

Abstract

Data stream mining is the process of extracting knowledge from continuous sequences of data. It differs from conventional data mining in that a stream is potentially unbounded, data points arrive online and each data point can be examined only once. Furthermore, in non-stationary environments the statistical properties of the data can change over time. This paper presents a bio-inspired approach to clustering non-stationary data streams. The proposed algorithm, Ant-Colony Stream Clustering (ACSC), is based on the concept of artificial ants which identify clusters as nests of micro-clusters in dense areas of the data. Micro-clusters are N-dimensional spheres with a maximum radius \(\varepsilon \). In ACSC the \(\varepsilon \)-neighbourhood, crucial in density clustering, is adaptive and doesn’t require expert, data-dependent tuning. The algorithm uses the sliding window model and summary statistics for each window are stored offline. Experimental results over real and synthetic datasets show that the clustering quality of ACSC is comparable or favourable to leading stream-clustering algorithms while requiring fewer parameters and considerably less computation.

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Notes

  1. 1.

    http://www.kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.

  2. 2.

    https://www.sites.google.com/site/nonstationaryarchive/.

References

  1. 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. VLDB ’03, VLDB Endowment (2003). http://dl.acm.org/citation.cfm?id=1315451.1315460

  2. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)

    Google Scholar 

  3. Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: SDM, vol. 6, pp. 328–339. SIAM (2006)

    Google Scholar 

  4. Tu, L., Chen, Y.: Stream data clustering based on grid density and attraction. ACM Trans. Knowl. Discov. Data (TKDD) 3(3), 12 (2009)

    Google Scholar 

  5. 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. from Data (TKDD) 3(3), 14 (2009)

    Article  Google Scholar 

  6. Forestiero, A., Pizzuti, C., Spezzano, G.: A single pass algorithm for clustering evolving data streams based on swarm intelligence. Data Min. Knowl. Discov. 26(1), 1–26 (2013)

    Article  MathSciNet  Google Scholar 

  7. Reynolds, C.W.: Flocks, herds and schools: A distributed behavioral model. In: ACM SIGGRAPH Computer Graphics, vol. 21, pp. 25–34. ACM (1987)

    Google Scholar 

  8. Masmoudi, N., Azzag, H., Lebbah, M., Bertelle, C., Ben Jemaa, M.: How to use ants for data stream clustering. In: Proceedings of 2015 IEEE Congress on Evolutionary Computation, pp. 656–663 (2015)

    Google Scholar 

  9. Labroche, N.: Fast ant-inspired clustering algorithm for web usage mining. In: Information Processing and Management of Uncertainty (2006)

    Google Scholar 

  10. Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chrétien, L.: The dynamics of collective sorting robot-like ants and ant-like robots. In: Proceedings of the 1st International Conference on Simulation of Adaptive Behavior From Animals to Animats, pp. 356–363 (1991)

    Google Scholar 

  11. Handl, J., Knowles, J., Dorigo, M.: Ant-based clustering and topographic mapping. Artif. Life 12(1), 35–62 (2006)

    Article  Google Scholar 

  12. Handl, J., Meyer, B.: Ant-based and swarm-based clustering. Swarm Intell. 1(2), 95–113 (2007)

    Article  Google Scholar 

  13. Hartmann, V.: Evolving agent swarms for clustering and sorting. In: Proceedings of the 7th Annual conference on Genetic and Evolutionary Computation, pp. 217–224. ACM (2005)

    Google Scholar 

  14. Dorigo, M., Birattari, M., Sttzle, T.: Ant colony optimization. Comput. Intell. Mag. IEEE 1(4), 28–39 (2006)

    Article  Google Scholar 

  15. Runkler, T.A.: Ant colony optimization of clustering models. Int. J. Intell. Syst. 20(12), 1233–1251 (2005)

    Article  MATH  Google Scholar 

  16. Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: Moa: massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)

    Google Scholar 

  17. Jardine, N., van Rijsbergen, C.J.: The use of hierarchic clustering in information retrieval. Inf. Storage Retr. 7(5), 217–240 (1971)

    Google Scholar 

  18. Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)

    Article  Google Scholar 

  19. Souza, V.M.A., Silva, D.F., Gama, J., Batista, G.E.A.P.A.: Data stream classification guided by clustering on nonstationary environments and extreme verification latency. In: Proceedings of SIAM International Conference on Data Mining, pp. 873–881 (2015)

    Google Scholar 

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Acknowledgments

This work was funded by the Engineering and Physical Sciences Research Council (EPSRC) of U.K. under Grant EP/K001310/1.

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Authors and Affiliations

  1. School of Computer Science and Informatics, Centre for Computational Intelligence (CCI), De Montfort University, The Gateway, Leicester, LE1 9BH, UK

    Conor Fahy & Shengxiang Yang

Authors
  1. Conor Fahy
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  2. Shengxiang Yang
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Correspondence to Conor Fahy .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University Bailrigg School of Computing and Communications, Lancaster, United Kingdom

    Plamen Angelov

  2. School of Computing, University of Portsmouth School of Computing, Portsmouth, Hampshire, United Kingdom

    Alexander Gegov

  3. School of Comp. Sci. & Digital Media, Robert Gordon University School of Comp. Sci. & Digital Media, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Ins. of Mathematics, Physics & Comp. Sci, Aberystwyth University Ins. of Mathematics, Physics & Comp. Sci, Aberystwyth, United Kingdom

    Qiang Shen

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Fahy, C., Yang, S. (2017). Dynamic Stream Clustering Using Ants. In: Angelov, P., Gegov, A., Jayne, C., Shen, Q. (eds) Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-46562-3_32

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  • DOI: https://doi.org/10.1007/978-3-319-46562-3_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46561-6

  • Online ISBN: 978-3-319-46562-3

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