Advertisement

A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm

  • Boris Lorbeer
  • Ana Kosareva
  • Bersant Deva
  • Dženan Softić
  • Peter Ruppel
  • Axel Küpper
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 529)

Abstract

Clustering algorithms are recently regaining attention with the availability of large datasets and the rise of parallelized computing architectures. However, most clustering algorithms do not scale well with increasing dataset sizes and require proper parametrization for correct results. In this paper we present A-BIRCH, an approach for automatic threshold estimation for the BIRCH clustering algorithm using Gap Statistic. This approach renders the global clustering step of BIRCH unnecessary and does not require knowledge on the expected number of clusters beforehand. This is achieved by analyzing a small representative subset of the data to extract attributes such as the cluster radius and the minimal cluster distance. These attributes are then used to compute a threshold that results, with high probability, in the correct clustering of elements. For the analysis of the representative subset we parallelized Gap Statistic to improve performance and ensure scalability.

References

  1. 1.
    Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Parzen, E., Tanabe, K., Kitagawa, G. (eds.) Selected Papers of Hirotugu Akaike, pp. 199–213. Springer, New York (1998)CrossRefGoogle Scholar
  2. 2.
    Burbeck, K., Nadjm-Tehrani, S.: Adaptive real-time anomaly detection with incremental clustering. Inf. Secur. Tech. Rep. 12(1), 56–67 (2007)CrossRefGoogle Scholar
  3. 3.
    Dash, M., Liu, H., Xu, X.: ‘\(1+1 > 2\)’: Merging distance and density based clustering. In: Proceedings of Seventh International Conference on Database Systems for Advanced Applications, 2001, pp. 32–39 (2001)Google Scholar
  4. 4.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. Ser. B (Methodol.) 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.) Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press (1996)Google Scholar
  6. 6.
    Ismael, N., Alzaalan, M., Ashour, W.: Improved multi threshold birch clustering algorithm. Int. J. Artif. Intell. Appl. Smart Devices 2(1), 1–10 (2014)CrossRefGoogle Scholar
  7. 7.
    Jordan, M.I., Bach, F.R.: Learning spectral clustering. In: Advances in Neural Information Processing Systems 16. MIT Press (2003)Google Scholar
  8. 8.
    Kumar, N.S.L.P., Satoor, S., Buck, I.: Fast parallel expectation maximization for gaussian mixture models on gpus using cuda. In: 11th IEEE International Conference on High Performance Computing and Communications, pp. 103–109 (2009)Google Scholar
  9. 9.
    Macqueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Math, Statistics, and Probability, vol. 1, pp. 281–297. University of California Press (1967)Google Scholar
  10. 10.
    Meng, X., Bradley, J.K., Yavuz, B., Sparks, E.R., Venkataraman, S., Liu, D., Freeman, J., Tsai, D.B., Amde, M., Owen, S., Xin, D., Xin, R., Franklin, M.J., Zadeh, R., Zaharia, M., Talwalkar, A.: MLlib: machine learning in apache spark. CoRR (2015)Google Scholar
  11. 11.
    Owen, S., Anil, R., Dunning, T., Friedman, E.: Mahout in Action. Manning Publications Co., Shelter Island (2011)Google Scholar
  12. 12.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Sugar, C.A.: Techniques for Clustering and Classification with Applications to Medical Problems. Ph.D. Dissertation, Department of Statistics, Stanford University (1998)Google Scholar
  15. 15.
    Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. Roy. Stat. Soc. B (Stat. Methodol.) 63(2), 411–423 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Zechner, M., Granitzer, M.: Accelerating k-means on the graphics processor via cuda. In: First International Conference on Intensive Applications and Services, pp. 7–15 (2009)Google Scholar
  17. 17.
    Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: a new data clustering algorithm and its applications. Data Min. Knowl. Disc. 1(2), 141–182 (1997)CrossRefGoogle Scholar
  18. 18.
    Zhou, B., Hansen, J.: Unsupervised audio stream segmentation and clustering via the bayesian information criterion. In: Proceedings of ISCLP-2000: International Conference of Spoken Language Processing, pp. 714–717 (2000)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Service-centric Networking, Telekom Innovation LaboratoriesTechnische Universität BerlinBerlinGermany

Personalised recommendations