A Dynamic Clustering Algorithm for Context Change Detection in Sensor-Based Data Stream System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)

Abstract

Sensor-based monitoring systems are growing enormously which lead to generation of real-time sensor data to a great extent. The classification and clustering of this data are a challenging task within the limited memory and time constraints. The overall distribution of data is changing over the time, which makes the task even more difficult. This paper proposes a dynamic clustering algorithm to find and detect the different contexts in a sensor-based system. It mines dynamically changing sensor streams for different contexts of the system. It can be used for detecting the current context as well as in predicting the coming context of a sensor-based system. The algorithm is able to find context states of different length in an online and unsupervised manner which plays a vital role in identifying the behavior of sensor-based system. The experiments results on real-world high-dimensional datasets justify the effectiveness of the proposed clustering algorithm. Further, discussion on how the proposed clustering algorithm works in sensor-based system is provided which will be helpful for domain experts.

Keywords

Clustering Context state Data streams Principal component analysis (PCA) Sensor-based system 

References

  1. 1.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Elsevier (2011)Google Scholar
  2. 2.
    Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 44 (2014)CrossRefGoogle Scholar
  3. 3.
    Dey, A.K.: Providing architectural support for building context-aware applications. Doctoral Dissertation, Georgia Institute of Technology (2000)Google Scholar
  4. 4.
    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 Endowment (2003)CrossRefGoogle Scholar
  5. 5.
    Cao, F., Estert, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: Proceedings of the 2006 SIAM International Conference on Data Mining, pp. 328–339. Society for Industrial and Applied Mathematics (2006)CrossRefGoogle Scholar
  6. 6.
    Chen, Y., Tu, L.: Density-based clustering for real-time stream data. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM (2007)Google Scholar
  7. 7.
    Silva, J.A., Faria, E.R., Barros, R.C., Hruschka, E.R., de Carvalho, A.C., Gama, J.: Data stream clustering: a survey. ACM Comput. Surv. (CSUR) 46–53 (2013)Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Qahtan, A.A., Alharbi, B., Wang, S., Zhang, X.: A pca-based change detection framework for multidimensional data streams: change detection in multidimensional data streams. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2015)Google Scholar
  10. 10.
    Mirsky, Y., Shapira, B., Rokach, L., Elovici, Y.: pcstream: a stream clustering algorithm for dynamically detecting and managing temporal contexts. In: Advances in Knowledge Discovery and Data Mining, vol. 2015, pp. 119–133. Springer (2015)CrossRefGoogle Scholar
  11. 11.
    Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. A 374(2065), 20150202 (2016)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Bache, K., Lichman, M.: UCI Machine Learning Repository. http://archive.ics.uci.edu/ml (2013)
  13. 13.
    Huerta, R., Mosqueiro, T., Fonollosa, J., Rulkov, N., Rodriguez-Lujan, I.: Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring. Chemom. Intell. Lab. Syst. 157, 169–176 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringVisvesvaraya National Institute of TechnologyNagpurIndia

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