Skip to main content
SpringerLink
Log in

Incorporating Unsupervised and Semi-supervised Learning in Min-Max Neuron Network for Clustering Data

  • Conference paper
  • First Online:
Advances in Engineering Research and Application (ICERA 2018)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 63))

Included in the following conference series:

Abstract

This paper proposes an improved fuzzy min-max neural network for data clustering. The proposed model incorporates both unsupervised and semi-supervised methods during training. The studies and experiments are limited to the extent of data clustering with the supplied number of clusters and spherical clusters model being not to cover each other. Our study was validated on published datasets and compared experimental results with fuzzy min-max neural networks applying to the clustering and classification problems given by the other researchers. Our solution has significantly improved the accuracy of classification with the small number of created hyperboxes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Simpson, P.K.: Fuzzy min-max neural networks-part 1: classification (1992)

    Google Scholar 

  2. Simpson, P.K.: Fuzzy min-max neural networks-part 2: Clustering. IEEE Trans. Fuzzy Syst. 1(1) (1993)

    Article  Google Scholar 

  3. Seera, M., Lim, C.P., Loo, C.K.: A modified fuzzy min-max neural network for data clustering and its application to power quality monitoring. Appl. Soft Comput. 28, 19–29 (2015)

    Article  Google Scholar 

  4. Quteishat. A.M,. Lim, C.P.: A modified fuzzy min-max neural network and its application to fault classification. Soft Comput. Ind. Appl., 179–188 (2007)

    Google Scholar 

  5. Nandedkar.: A granular reflex fuzzy min–max neural network for classification. IEEE Trans. Neural Networks 20(7), 1117–1134 (2009)

    Google Scholar 

  6. Tolia, P.: Compensatory fuzzy min-max neural network for object recognition. IJCSN Int. J. Comput. Sci. Network 2(3), 42–47 (2013)

    Google Scholar 

  7. Davtalab, R., Parchami, M., Dezfoulian, M.H., Mansourizade, M., Akhtar, B.: M-FMCN: modified fuzzy min-max classifier using compensatory neurons. In: Proceeding in AIKED 2012, pp. 77–82 (2012)

    Google Scholar 

  8. Chaudhari, B.M., Patil, R.S., Rane, K.P., Shinde, U.B.: Online signature classification using modified fuzzy min-max neural network with compensatory neuron topology, pp. 467–478 (2010)

    Google Scholar 

  9. Mohammed, M.F.: An enhanced fuzzy min-max neural network for pattern classification. IEEE 26(3), 417–429 (2015)

    Article  MathSciNet  Google Scholar 

  10. Quteishat, A.: Application of the fuzzy min-max neural networks to medical diagnosis. In: Knowledge-Based Intelligent Information and Engineering Systems, LNCS of Springer, 5179, pp. 548–555 (2008)

    Google Scholar 

  11. Seera, M.: Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM–CART model. ‎IEEE Trans. Neural Netw. Learn. Syst. 23(1), 97–108 (2012)

    Article  Google Scholar 

  12. Zhang, H., Liu, J., Ma, D., Wang, Z.: Data-core-based fuzzy min–max neural network for pattern classification. ‎IEEE Trans. Neural Netw. 22(12), 2339–2352 (2011)

    Article  Google Scholar 

  13. Davtalab, R.: Multilevel fuzzy min-max neural network classifier. IEEE Trans. Neural Netw. Learn. Syst. 25(3), 470–482 (2014)

    Article  Google Scholar 

  14. Gabrys, B.: General fuzzy min-max neural network for clustering and classification. IEEE Trans. Neural Netw. 11(3), 769–783 (2000)

    Article  Google Scholar 

  15. Nandedkar, A.V.: Reflex fuzzy min max neural network for semi-supervised learning. Int. J. Intell. 17(1–3), 5–18 (2011)

    Google Scholar 

  16. Quteishat, A.: A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification. Appl. Soft Comput. 8(2), 985–995 (2008)

    Article  Google Scholar 

  17. Quteishat, A.: A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification. IIEEE Trans. Syst. Man Cybern - Part A Syst. Hum. 40(3), 641–650 (2010)

    Article  Google Scholar 

  18. Shinde, S.: Diabetes diagnosis using fuzzy min-max neural network with rule extraction and Apriori algorithm (2014)

    Google Scholar 

  19. Wang, J.: Patient admission prediction using a pruned fuzzy min–max neural network with rule extraction. Neural Comput. Appl. 26(2), 277–289 (2015)

    Article  Google Scholar 

  20. Minh, V.D.: An Increased Fuzzy Min-Max Neural Network for Data Clustering (2016)

    Google Scholar 

  21. Kulkarni, S., Honwadkar, K.: Review on classification and clustering using fuzzy neural networks. Int. J. Comput. Appl. (0975 – 8887), 136(3), 18–23 (2016)

    Article  Google Scholar 

  22. Jain, B., Kolhe, V.: Survey on fuzzy min-max neural network classification. Int. J. Adv. Res. Comput. Commun. Eng. (IJARCCE) 4(12), 30–34 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ha Long University, Ha Long City, Quang Ninh, Vietnam

    Le Anh Tu

  2. University of Information and Communication Technology, Thainguyen, Vietnam

    Vu Duc Thai

  3. Thai Nguyen Industrial College, Thai Nguyen, Vietnam

    Vu Dinh Minh

Authors
  1. Le Anh Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vu Duc Thai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vu Dinh Minh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Le Anh Tu , Vu Duc Thai or Vu Dinh Minh .

Editor information

Editors and Affiliations

  1. Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, Iwate, Japan

    Hamido Fujita

  2. Faculty of Electronic Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam

    Duy Cuong Nguyen

  3. Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam

    Ngoc Pi Vu

  4. Hanoi University of Science and technology, Hanoi, Vietnam

    Tien Long Banh

  5. Institute for Automation and Systems Engineering, Ilmenau University of Technology (TU Ilmenau), Ilmenau, Germany

    Hermann Horst Puta

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tu, L.A., Thai, V.D., Minh, V.D. (2019). Incorporating Unsupervised and Semi-supervised Learning in Min-Max Neuron Network for Clustering Data. In: Fujita, H., Nguyen, D., Vu, N., Banh, T., Puta, H. (eds) Advances in Engineering Research and Application. ICERA 2018. Lecture Notes in Networks and Systems, vol 63. Springer, Cham. https://doi.org/10.1007/978-3-030-04792-4_47

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04792-4_47

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04791-7

  • Online ISBN: 978-3-030-04792-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation