Semi-supervised Learning Algorithm for Online Electricity Data Streams

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)

Abstract

Recent developments in electricity market deregulation, the prices are not fixed. In such application, class labels are not available directly and potentially valuable information is lost. A learning model of electricity demand and prices needs to be adaptive for dynamic changes in massive data streams. This paper presents adaptive building of learning model for electricity demand supply and prices by detecting and adapting changes in trends and values. A proposed framework is build with four main challenges such as future assumptions, data stream summarization, change in data stream trend clusters, and learner adaptivity and model. A proposed online algorithm for not only considering data values by avoiding trends of the streams. A correlation-based similarity method is used to produce concept clusters to handle unlabeled data and trend analysis, change detection type in terms of variation between past concept clusters and current ones, and predict future assumptions. An adaptive classify algorithm for the predictive ability evaluation on the test set. Results of experiments using electricity data confirm applicability of methodology with more than 80–85 % unlabeled data.

Keywords

Multiple data streams Data summarization On-line update Synopsis Single scan Clustering 

References

  1. 1.
    M.R. Anderberg, Cluster Analysis for Applications (Academic Press Inc, New York, NY, 1973)MATHGoogle Scholar
  2. 2.
    J. Yang, Dynamic clustering of evolving streams with a single pass, in Proceedings The 19th International Conference on Data Engineering (2003), pp. 695–697Google Scholar
  3. 3.
    J. Beringer, E. Hüllermeier, Online-clustering of parallel data streams. Data Knowl. Eng. 58(2), 180–204 (2006)CrossRefGoogle Scholar
  4. 4.
    L. O’Callaghan, N. Mishra, A. Meyerson, S. Guha, R. Motwani, Streaming-data algorithms for high quality clustering, in Proceedings of IEEE International Conference on Data Engineering (2002)Google Scholar
  5. 5.
    C. Aggarwal, J. Han, J. Wang, P.S. Yu, A framework for clustering evolving data streams, in Proceedings 2003 International Conference on Very Large Data Bases (2003)Google Scholar
  6. 6.
    C. Aggarwal, J. Han, J. Wang, P.S. Yu, A framework for projected clustering of high dimensional data streams, in Proceedings of 2004 International Conference on Very Large Data Bases (2004)Google Scholar
  7. 7.
    E. Keogh, J. Lin, W. Truppel, Clustering of time series subsequences is meaningless: implications for past and future research, in Proceedings of the 3rd IEEE International Conference on Data Mining (2003)Google Scholar
  8. 8.
    B.R. Dai, J.W. Huang, M.Y. Yeh, M.S. Chen, Adaptive clustering for multiple evolving streams. IEEE Trans. Knowl. Data Eng. 18(9) (2006)Google Scholar
  9. 9.
    M.-Y. Yeh, B.-R. Dai, M.-S. Chen, Clustering over multiple evolving streams by events and correlations. IEEE Trans. Knowl. Data Eng. 19(10) (2007)Google Scholar
  10. 10.
    L. Kaufmann, P. Rousseeuw, Finding groups in data: an introductionGoogle Scholar
  11. 11.
    R. Ng, J. Hahn, Efficient and effective clustering methods for spatial data mining (1994)Google Scholar
  12. 12.
    T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: A new data clustering algorithm and its applications. Data Min. Knowl. Discov. 1, 141–182 (1997)Google Scholar
  13. 13.
    S. Guha, A. Meyerson, N. Mishra, R. Motwani, Clustering data streams: theory and practice. IEEE Trans. Knowl. Data Eng. 15(3), 515–528 (2003)CrossRefGoogle Scholar
  14. 14.
    C.C. Aggarwal, J. Han, J. Wang, P.S. Yu, A framework for clustering evolving data streams, in Proceedings of conference of very large databases (2003), pp. 81–92Google Scholar
  15. 15.
    A. Franzblau, A Primer of Statistics for Non-Statisticians (Harcourt, Brace, and World, California, 1958)Google Scholar
  16. 16.
    S. Guha, N. Mishra, R. Motwani, L. O’Callaghan, Clustering data streams, in Proceedings of Annual Symposium Foundations of Computer Science (2000)Google Scholar
  17. 17.
    C.C. Aggarwal, J. Han, J. Wang, P.S. Yu, A Framework for clustering evolving data streams, in Proceedings of Very Large Data Bases Conference (2003)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Pramod Patil
    • 1
  • Yogita Fatangare
    • 1
  • Parag Kulkarni
    • 1
  1. 1.Department of Computer EngineeringCollege of EngineeringPuneIndia

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