SEQUENTIAL FUZZY CLUSTERING BASED ON NEURO-FUZZY APPROACH

Authors

  • Ye. V. Bodyanskiy Kharkiv National University of Radio Electronics, Kharkiv, Ukraine, Ukraine
  • A. O. Deineko Kharkiv National University of Radio Electronics, Kharkiv, Ukraine, Ukraine
  • Ya. V. Kutsenko Kharkiv National University of Radio Electronics, Kharkiv, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2016-3-4

Keywords:

hybrid system, Data Mining, Data Stream Mining, neuro-fuzzy system, membership function, fuzzy clustering.

Abstract

An on-line neuro-fuzzy system for solving data stream fuzzy clustering task and its self-learning procedures based on T. Kohonen’s rule are proposed in the paper. The architecture of proposed system consists of seven information processing layers and represents the hybrid of the Wang-Mendel system and clustering selforganizing network. During a learning procedure in on-line mode, the proposed system tunes both its parameters and its architecture. For tuning of membership
functions parameters of neuro-fuzzy system the method based on competitive learning is proposed. The hybrid neuro-fuzzy system tunes its synaptic weights, centers and width parameters of membership functions. Software that implements the proposed hybrid neuro-fuzzy system’s architecture has been developed. A number of experiments has been held in order to research the proposed system’s properties. Experimental results have proved the fact that the proposed system could be used to solve a sequential stream clustering task. The proposed system provides computational simplicity. A distinguishing feature of the proposed system is that this system combine supervised learning and self-learning procedures.

References

Rutkowski L. Computational Intelligence. Methods and Techniques / L. Rutkowski. – Berlin-Heidelberg : Springer-Verlag, 2008. – 514 p. 2. Mumford C. Computational Intelligence. Collaboration, Fuzzy and Emergence / C. Mumford, L. Jain. – Berlin: Springer-Vergal, 2009. – 726 p. 3. Computational Intelligence. A Methodological Introduction / [R. Kruse, C. Borgelt, F. Klawonn and other]. – Berlin : Springer, 2013. – 488 p. 4. Du K.-L. Neural Networks and Statistical Learning / K.-L. Du, M. N. S. Swamy. – London : Springer-Verlag, 2014. – 824 p. 5. Han J. Data Mining : Concepts and Techniques / J. Han, M. Kamber. – Amsterdam : Morgan Kaufmann Publ., 2006. – 754 p. 6. Aggarwal C. C. Data Mining / A. A. Aggarwal. – Cham : Springer, Int. Publ., Switzerland, 2015. – 734 p. 7. Aggarwal C. C. Data Clustering. Algorithms and Application / C. C. Aggarwal, C. K. Reddy. – Boca Raton : CRC Press, 2014. – 648 p. 8. Xu R. Clustering, IEEE Press Series on Computational Intelligence / R. Xu, D. C. Wunsch. – Hoboken, NJ: John Wiley & Sons, Inc., 2009. – 370 p. 9. Haykin S. Neural Networks. A Comprehensive Foundation / S. Haykin. – Upper Saddle River, N.J.: Prentice Hall, Inc., 1999. – 842 p. 10. Bifet A. Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams / A. Bifet. – IOS Press, 2010. – 224 p. 11. Kohonen, T. Self-Organizing Maps / T. Kohonen. – Berlin : Springer-Verlag, 1995. – 362 p. 12. H ppner F. Fuzzy-Clusteranalyse: Verfahren f r die Bilderkennung, Klassifikation und Datenanalyse. / F. H ppner, F. Klawonn, R. Kruse. – Braunschweig : Vieweg, 1996. – 280 p. 13. Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition [F. H ppner, F. Klawonn, R. Kruse, T. Runkler]. – Chichester : John Wiley & Sons, 1999. – 289 p.

Downloads

How to Cite

Bodyanskiy, Y. V., Deineko, A. O., & Kutsenko, Y. V. (2016). SEQUENTIAL FUZZY CLUSTERING BASED ON NEURO-FUZZY APPROACH. Radio Electronics, Computer Science, Control, (3). https://doi.org/10.15588/1607-3274-2016-3-4

Issue

No. 3 (2016): Radio Electronics, Computer Science, Control

Section

Neuroinformatics and intelligent systems

License

Copyright (c) 2016 Ye. V. Bodyanskiy, A. O. Deineko, Ya. V. Kutsenko

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons Licensing Notifications in the Copyright Notices

The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.

The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.

The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.