Skip to main content
Book cover

Industrial Conference on Data Mining

ICDM 2018: Advances in Data Mining. Applications and Theoretical Aspects pp 173–182Cite as

Dynamic Classifier and Sensor Using Small Memory Buffers

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10933)

Abstract

A model presented in current paper designed for dynamic classifying of real time cases received in a stream of big sensing data. The model comprises multiple remote autonomous sensing systems; each generates a classification scheme comprising a plurality of parameters. The classification engine of each sensing system is based on small data buffers, which include a limited set of “representative” cases for each class (case-buffers). Upon receiving a new case, the sensing system determines whether it may be classified into an existing class or it should evoke a change in the classification scheme. Based on a threshold of segmentation error parameter, one or more case-buffers are dynamically regrouped into a new composition of buffers, according to a criterion of segmentation quality.

Keywords

  • Dynamic classifier
  • Dynamic rules
  • Big data
  • Sensing data
  • Memory buffers
  • Clustering
  • Classification

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-95786-9_13
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-95786-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   79.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

References

  1. Fayyad, U., Stolorz, P.: Data mining and KDD: promise and challenges. Future Gener. Comput. Syst. 13, 99–115 (1997). https://doi.org/10.1016/S0167-739X(97)00015-0

    CrossRef  Google Scholar 

  2. Gelbard, R., Goldman, O., Spieger, I.: Investigating diversity of clustering methods: an empirical comparison. Data Knowl. Eng. 63, 155–166 (2007)

    CrossRef  Google Scholar 

  3. Fan, J., Han, F., Han, L.: Challenges of big data analysis. Natl. Sci. Rev., 293–314 (2014)

    CrossRef  Google Scholar 

  4. Darema, F.: Dynamic data driven applications systems: a new paradigm for application simulations and measurements. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 662–669. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24688-6_86

    CrossRef  Google Scholar 

  5. Ren, K., Wang, C., Wang, Q.: Security challenges for the public cloud. IEEE Internet Comput. 16, 69–73 (2012). https://doi.org/10.1109/MIC.2012.14

    CrossRef  Google Scholar 

  6. Li, J., Xu, H.: Suggest what to tag: recommending more precise hashtags based on users’ dynamic interests and streaming tweet content. Knowl.-Based Syst. 106, 196–205 (2016). https://doi.org/10.1016/j.knosys.2016.05.047

    CrossRef  Google Scholar 

  7. Miller, Z., Dickinson, B., Deitrick, W., et al.: Twitter spammer detection using data stream clustering. Inf. Sci. 260, 64–73 (2014). https://doi.org/10.1016/j.ins.2013.11.016

    CrossRef  Google Scholar 

  8. Siddharth, S., Chauhan, N.C., Bhandery, S.D.: Incremental mining of association rules: a survey. Int. J. Comput. Sci. Inf. Technol. 3, 4041–4074 (2012)

    Google Scholar 

  9. Cheung, D.W., Han, J., Ng, V.T., Wong, C.: Maintenance of discovered association rules in large databases: An Incremental Updating Technique, pp. 106–114 (1996)

    Google Scholar 

  10. Grira, N., Crucianu, M., Boujemaa, N.: Unsupervised and semi-supervised clustering: a brief survey. In: Review of Machine Learning Techniques for Processing Multimedia Content (2005)

    Google Scholar 

  11. Sreedhar, G.: Web Data Mining and the Development of Knowledge-Based Decision Support Systems. IGI Global, Hershey (2016)

    Google Scholar 

  12. Song, Y.C., Meng, H.D., Wang, S.L., et al.: Dynamic and incremental clustering based on density reachable. In: Fifth International Joint Conference on INC, IMS and IDC, 2009. NCM 2009, pp. 1307–1310 (2009)

    Google Scholar 

  13. Mishra, N., Hsu, M., Dayal, U.: Computer implemented scalable, incremental and parallel clustering based on divide and conquer (2002)

    Google Scholar 

  14. Deza, M.M., Deza, E.: Encyclopedia of Distances. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44342-2

    CrossRef  MATH  Google Scholar 

  15. Jain, A.K., Murty, M.N., Flynn, P.L.: Data clustering: a survey. ACM Comput. Surv. 31, 264–323 (1999)

    CrossRef  Google Scholar 

  16. Vempala, S., Wang, G., Kannan, R., Cheng, D.: Techniques for clustering a set of objects (2010)

    Google Scholar 

  17. Thomas, S., Bodagala, S., Alsabti, K., Ranka, S.: An efficient algorithm for the incremental updation of association rules in large databases (1997)

    Google Scholar 

  18. Zhang, C-Q., Ou, Y.: Method for data clustering and classification by a graph theory model - network partition into high density subgraphs (2009)

    Google Scholar 

  19. Lughofer, E.: A dynamic split-and-merge approach for evolving cluster models. Evol. Syst. 3, 135–151 (2012). https://doi.org/10.1007/s12530-012-9046-5

    CrossRef  Google Scholar 

  20. Toth, C.K., Grejner-Brzezinska, D.: Extracting dynamic spatial data from airborne imaging sensors to support traffic flow estimation. ISPRS J. Photogramm. Remote Sens. 61, 137–148 (2006). https://doi.org/10.1016/j.isprsjprs.2006.09.010

    CrossRef  Google Scholar 

  21. Zhang, Y., He, S., Chen, J.: Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks. IEEE/ACM Trans. Netw. 24, 1632–1646 (2016). https://doi.org/10.1109/TNET.2015.2425146

    CrossRef  Google Scholar 

  22. Okeyo, G., Chen, L., Wang, H., Sterritt, R.: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive Mob. Comput. 10, 155–172 (2014). https://doi.org/10.1016/j.pmcj.2012.11.004

    CrossRef  Google Scholar 

  23. Ni, Q., Patterson, T., Cleland, I., Nugent, C.: Dynamic detection of window starting positions and its implementation within an activity recognition framework. J. Biomed. Inform. 62, 171–180 (2016). https://doi.org/10.1016/j.jbi.2016.07.005

    CrossRef  Google Scholar 

  24. Download Python. In: Python.org. https://www.python.org/downloads/. Accessed 20 Apr 2018

  25. Ben-David, A.: Automatic generation of symbolic multiattribute ordinal knowledge - based DSS’s: methodology and applications. Decis. Sci. 23, 1357–1372 (1992)

    CrossRef  Google Scholar 

  26. UCI Machine Learning Repository: Data Sets. https://archive.ics.uci.edu/ml/datasets.html. Accessed 10 Dec 2016

  27. Bohanec, M., Rajkovic, V.: Knowledge acquisition and explanation for multi-attribute decision making, pp. 1–19 (1988)

    Google Scholar 

  28. Bittmann, R.M., Gelbard, R.: Visualization of multi-algorithm clustering for better economic decisions—the case of car pricing. Decis. Support Syst. 47, 42–50 (2009). https://doi.org/10.1016/j.dss.2008.12.012

    CrossRef  Google Scholar 

Download references

Acknowledgment

This work was supported in part by a grant from the MAGNET program of the Israeli Innovation Authority; who also submitted this work as a patent application.

Author information

Authors and Affiliations

  1. Information Systems, Bar-Ilan University, Ramat-Gan, 5290002, Israel

    R. Gelbard & A. Khalemsky

Authors
  1. R. Gelbard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Khalemsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to R. Gelbard or A. Khalemsky .

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Gelbard, R., Khalemsky, A. (2018). Dynamic Classifier and Sensor Using Small Memory Buffers. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2018. Lecture Notes in Computer Science(), vol 10933. Springer, Cham. https://doi.org/10.1007/978-3-319-95786-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95786-9_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95785-2

  • Online ISBN: 978-3-319-95786-9

  • eBook Packages: Computer ScienceComputer Science (R0)