Advertisement

Exploring Data Mining Techniques in Medical Data Streams

  • Le SunEmail author
  • Jiangang Ma
  • Yanchun Zhang
  • Hua Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9877)

Abstract

Data stream mining has been studied in diverse application domains. In recent years, a population aging is stressing the national and international health care systems. Anomaly detection is a typical example of a data streams application. It is a dynamic process of finding abnormal behaviours from given data streams. In this paper, we discuss the existing anomaly detection techniques for Medical data streams. In addition, we present a process of using the Autoregressive Integrated Moving Average model (ARIMA) to analyse the ECG data streams.

Keywords

Data Stream Anomaly Detection ARIMA Model Multiple Kernel Learning Reference Window 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Brauckhoff, D., Salamatian, K., May, M.: A signal processing view on packet sampling and anomaly detection. In: 2010 Proceedings IEEE INFOCOM, pp. 1–9, March 2010Google Scholar
  2. 2.
    Budalakoti, S., Budalakoti, S., Srivastava, A., Otey, M., Otey, M.: Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 39(1), 101–113 (2009)CrossRefGoogle Scholar
  3. 3.
    Buja, A., Hastie, T., Tibshirani, R.: Linear smoothers and additive models. Ann. Stat. 17, 453–510 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection for discrete sequences: a survey. IEEE Trans. Knowl. Data Eng. 24(5), 823–839 (2012)CrossRefGoogle Scholar
  5. 5.
    Chandola, V., Mithal, V., Kumar, V.: Comparative evaluation of anomaly detection techniques for sequence data. In: IEEE International Conference on Data Mining, pp. 743–748 (2008)Google Scholar
  6. 6.
    Council, C.S.C.: Impact of cloud computing on healthcare. Technical report, Cloud Standards Customer Council, November 2012Google Scholar
  7. 7.
    Das, S., Matthews, B.L., Srivastava, A.N., Oza, N.C.: Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 47–56. ACM (2010)Google Scholar
  8. 8.
    Dasgupta, D., Majumdar, N.: Anomaly detection in multidimensional data using negative selection algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 2, pp. 1039–1044 (2002)Google Scholar
  9. 9.
    Dasgupta, D., Nino, F.: A comparison of negative and positive selection algorithms in novel pattern detection. In: 2000 IEEE International Conference on Systems, Man, and Cybernetics, vol. 1, pp. 125–130 (2000)Google Scholar
  10. 10.
    Durairaj, M., Ranjani, V.: Data mining applications in healthcare sector a study. Int. J. Sci. Technol. Res. 2(10) (2013)Google Scholar
  11. 11.
    Forrest, S., Hofmeyr, S.A., Somayaji, A., Longstaff, T.A.: A sense of self for unix processes. In: Proceedings of the 1996 IEEE Symposium on Security and Privacy, SP 1996, p. 120 (1996). http://dl.acm.org/citation.cfm?id=525080.884258
  12. 12.
    Gao, B., Ma, H.Y., Yang, Y.H.: Hmms (hidden markov models) based on anomaly intrusion detection method. In: Proceedings of the 2002 International Conference on Machine Learning and Cybernetics, vol. 1, pp. 381–385 (2002)Google Scholar
  13. 13.
    Ghosh, A.K., Schwartzbard, A., Schatz, M.: Learning program behavior profiles for intrusion detection. In: Proceedings of the 1st Conference on Workshop on Intrusion Detection and Network Monitoring, ID’99, vol. 1, p. 6. USENIX Association, Berkeley (1999). http://dl.acm.org/citation.cfm?id=1267880.1267886
  14. 14.
    Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)CrossRefGoogle Scholar
  15. 15.
    Gower, J.C.: A general coefficient of similarity and some of its properties. Biometrics 27, 857–871 (1971)CrossRefGoogle Scholar
  16. 16.
    Hofmeyr, S.A., Forrest, S., Somayaji, A.: Intrusion detection using sequences of system calls. J. Comput. Secur. 6(3), 151–180. http://dl.acm.org/citation.cfm?id=1298081.1298084 Google Scholar
  17. 17.
    Jacoby, W.G.: Loess: a nonparametric, graphical tool for depicting relationships between variables. Electoral. Stud. 19(4), 577–613 (2000)CrossRefGoogle Scholar
  18. 18.
    Khalid, S.: Activity classification and anomaly detection using m-mediods based modelling of motion patterns. Pattern Recogn. 43(10), 3636–3647 (2010). http://www.sciencedirect.com/science/article/pii/S0031320310002074 Google Scholar
  19. 19.
    Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing sax: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2), 107–144 (2007). http://dx.doi.org/10.1007/s10618-007-0064-z Google Scholar
  20. 20.
    Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data 6(1), 3:1–3:39. http://doi.acm.org/10.1145/2133360.2133363 Google Scholar
  21. 21.
    Liu, W., Hua, G., Smith, J.: Unsupervised one-class learning for automatic outlier removal. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3826–3833, June 2014Google Scholar
  22. 22.
    Masud, M., Gao, J., Khan, L., Han, J., Thuraisingham, B.: Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Trans. Knowl. Data Eng. 23(6), 859–874 (2011)CrossRefGoogle Scholar
  23. 23.
    Pavlov, D.: Sequence modeling with mixtures of conditional maximum entropy distributions. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 251–258, November 2003Google Scholar
  24. 24.
    Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD 2000, pp. 427–438. ACM, New York (2000). http://doi.acm.org/10.1145/342009.335437 Google Scholar
  25. 25.
    Silveira, F., Diot, C., Taft, N., Govindan, R.: Astute: Detecting a different class of traffic anomalies. In: Proceedings of the ACM SIGCOMM 2010 Conference, SIGCOMM 2010, pp. 267–278. ACM, New York (2010). http://doi.acm.org/10.1145/1851182.1851215
  26. 26.
    Tan, S.C., Ting, K.M., Liu, T.F.: Fast anomaly detection for streaming data. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI 2011, vol. 2, pp. 1511–1516. AAAI Press (2011). http://dx.doi.org/10.5591/978-1-57735-516-8/IJCAI11-254
  27. 27.
    Warrender, C., Forrest, S., Pearlmutter, B.: Detecting intrusions using system calls: alternative data models. In: Proceedings of the 1999 IEEE Symposium on Security and Privacy, pp. 133–145 (1999)Google Scholar
  28. 28.
    Wei, W.W.S.: Time series analysis. Addison-Wesley publ. Reading (1994)Google Scholar
  29. 29.
    Yang, J., Wang, W.: Cluseq: efficient and effective sequence clustering. In: Proceedings. 19th International Conference on Data Engineering, 2003, pp. 101–112, March 2003Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Centre for Applied InformaticsVictoria UniversityMelbourneAustralia

Personalised recommendations