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On Pre-processing Algorithms for Data Stream

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Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7268))

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Abstract

Clustering is a one of the most important tasks of data mining. Algorithms like the Fuzzy C-Means and Possibilistic C-Means provide good result both for the static data and data streams. All clustering algorithms compute centers from chunk of data, what requires a lot of time. If the rate of incoming data is faster than speed of algorithm, part of data will be lost. To prevent such situation, some pre-processing algorithms should be used. The purpose of this paper is to propose a pre-processing method for clustering algorithms. Experimental results show that proposed method is appropriate to handle noisy data and can accelerate processing time.

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References

  1. Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proceedings of 29th International Conference on Very Large Data Bases, pp. 81–92 (2003)

    Google Scholar 

  2. Birant, D., Kut, A.: ST-DBSCAN: An algorithm for clustering spatial-temporal data. In: Data and Knowledge Engineering, pp. 208–221 (2007)

    Google Scholar 

  3. Domingos, P., Hulton, G.: A general method for scaling up machine learning algorithms and its application to clustering. In: Proceedings of the 18th International Conference on Machine Learning, pp. 106–113 (2001)

    Google Scholar 

  4. Babcook, B., Badu, S., Datar, M., Motwani, R., Widom, J.: Models and Issues in Data Stream Systems. In: International Conference on Management of Data and Symposium on Principles Database and Systems Madison (2002)

    Google Scholar 

  5. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algoritms. Plenum Press, New York (1981)

    Google Scholar 

  6. Golab, L., Ozsu, T.: Issue in Data Stream Management. SIGMOD Record 32(2) (2003)

    Google Scholar 

  7. Guha, S., Koudas, N., Shim, K.: Data-Streams and Histograms. STOC 2001, Hersonissos, Crete, Greece (2001)

    Google Scholar 

  8. Hoppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Cluster Analysis. In: Method For Classification, Data Analysis and Image Recognition. John Wiley & Sons, Ltd. (1999)

    Google Scholar 

  9. Jagadish, H.V., Koudas, N., Muthukrishnan, S., Poosala, V., Sevcik, K., Suel, T.: Optimal Histograms with Quality Guarantees. In: Proceeding of the 24th VLBD Conference, New York, USA (1998)

    Google Scholar 

  10. Krishnapuram, R., Keller, J.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1(2), 98–110 (1993)

    Article  Google Scholar 

  11. Tu, L., Chen, Y.: Stream Data Clustering based on Grid Density and Attraction. ACM Transactions on Computional Logic 1, 1–26 (2008)

    Google Scholar 

  12. MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)

    Google Scholar 

  13. Miyamoto, S., Ichihashi, H., Honda, K.: Algorithms for Fuzzy Clustering. Methods in C-Means Clustering with Applications. Springer (2008)

    Google Scholar 

  14. Park, N.H., Lee, W.S.: Statistical Grid-based Clustering over Data Streams. SIGMOD Record 33(1) (2004)

    Google Scholar 

  15. Nowicki, R.: Nonlinear modelling and classification based on the MICOG defuzzifications. Journal of Nonlinear Analysis, Series A: Theory, Methods and Applications 7(12), 1033–1047 (2009)

    Article  Google Scholar 

  16. O’ Chalaghan, L., Mishra, N., Meyerson, A., Guha, S., Motwani, R.: Streaming data algorithms for high quality clustering. In: Proceedings of the 18th International Conference on Data Engineering, pp. 685–694 (2002)

    Google Scholar 

  17. Poosala, V., Ioannidis, Y.E., Haas, P.J., Shekita, E.J.: Improved Histograms for Selectivity Estimation of Range Predicates. In: Proc. of ACM SIGMOD Conf., pp. 294–305 (1996)

    Google Scholar 

  18. Rutkowski, L.: The real-time identification of time-varying systems by nonparametric algorithms based on the Parzen kernels. International Journal of Systems Science 16, 1123–1130 (1985)

    Article  MATH  Google Scholar 

  19. Rutkowski, L.: Sequential pattern recognition procedures derived from multiple Fourier series. Pattern Recognition Letters 8, 213–216 (1988)

    Article  MATH  Google Scholar 

  20. Rutkowski, L.: An application of multiple Fourier series to identification of multivariable nonstationary systems. International Journal of Systems Science 20(10), 1993–2002 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  21. Rutkowski, L.: Nonparametric learning algorithms in the time-varying environments. Signal Processing 18, 129–137 (1989)

    Article  MathSciNet  Google Scholar 

  22. Rutkowski, L.: Computational Intelligence: Methods and Techniques. Springer (2008)

    Google Scholar 

  23. Rutkowski, L., Cpałka, K.: A general approach to neuro - fuzzy systems. In: Proceedings of the 10th IEEE International Conference on Fuzzy Systems, Melbourne, December 2-5, vol. 3, pp. 1428–1431 (2001)

    Google Scholar 

  24. Rutkowski, L., Cpałka, K.: A neuro-fuzzy controller with a compromise fuzzy reasoning. Control and Cybernetics 31(2), 297–308 (2002)

    MATH  Google Scholar 

  25. Scherer, R.: Boosting Ensemble of Relational Neuro-fuzzy Systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 306–313. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  26. Scherer, R.: Neuro-fuzzy Systems with Relation Matrix. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6113, pp. 210–215. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  27. Starczewski, J., Rutkowski, L.: Interval type 2 neuro-fuzzy systems based on interval consequents. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing, pp. 570–577. Physica-Verlag, Springer-Verlag Company, Heidelberg, New York (2003)

    Google Scholar 

  28. Starczewski, J.T., Rutkowski, L.: Connectionist Structures of Type 2 Fuzzy Inference Systems. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds.) PPAM 2001. LNCS, vol. 2328, pp. 634–642. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  29. Vivekanandan, P., Nedunchezhian, R.: Mining Rules of Concept Drift Using Genetic Algorithm. Journal of Artificial Inteligence and Soft Computing Research 1(2), 135–145 (2011)

    Google Scholar 

  30. Wan, R., Yan, X., Su, X.: A Weighted Fuzzy Clustering Algorithm for Data Stream. In: ISECS International Colloquium on Computing, Communication, Control, and Management (2008)

    Google Scholar 

  31. Zhang, D., Gunopulos, D., Tsotras, V.J., Seeger, B.: Temporal and spatio-temporal aggregations over data streams using multiple time granularities. Journal Information Systems 28(1-2) (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland

    Piotr Duda, Maciej Jaworski & Lena Pietruczuk

Authors
  1. Piotr Duda
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  2. Maciej Jaworski
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  3. Lena Pietruczuk
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Duda, P., Jaworski, M., Pietruczuk, L. (2012). On Pre-processing Algorithms for Data Stream. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_7

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  • DOI: https://doi.org/10.1007/978-3-642-29350-4_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29349-8

  • Online ISBN: 978-3-642-29350-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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