Skip to main content

Advertisement

SpringerLink
Log in

Application of genetic algorithm-based intuitionistic fuzzy weighted c-ordered-means algorithm to cluster analysis

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

With the advance of information technology, many fields have begun using data clustering to reveal data structures and obtain useful information. Most of the existing clustering algorithms are susceptible to outliers and noises as well as the initial solution. The fuzzy c-ordered-means (FCOM) method can handle outlier and noise problems by using Huber’s M-estimators and Yager’s OWA operator to enhance its robustness. However, the result of the FCOM algorithm is still unstable because its initial centroids are randomly generated. Besides, the attributes’ weight also affect the clustering performance. Thus, this study first proposed an intuitionistic fuzzy weighted c-ordered-means (IFWCOM) algorithm that combines intuitionistic fuzzy sets (IFSs), the feature-weighted and FCOM together to improve the clustering result. Moreover, this study proposed a real-coded genetic algorithm-based IFWCOM (GA-IFWCOM) that employs the genetic algorithm to exploit the global optimal solution of the IFWCOM algorithm. Twelve benchmark datasets were used for verification in the experiment. According to the experimental results, the GA-IFWCOM algorithm achieved better clustering accuracy than the other clustering algorithms for most of the datasets.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    Article  Google Scholar 

  2. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers

    Book  Google Scholar 

  3. Bezdek JC, Boggavaparu S, Hall LO, Bensaid A (1994) Genetic algorithm guided clustering. In: Proc. 1st IEEE Conf. Evol. Comput., Orlando, FL, pp 34–39

  4. Birgin EG, Martínez JM, Raydan M (2000) Nonmonotone spectral projected gradient methods on convex sets. SIAM J Optim 10(4):1196–1211

    Article  MathSciNet  Google Scholar 

  5. Chaira T (2011) A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images. Appl Soft Comput 11(2):1711–1717

    Article  Google Scholar 

  6. Dimitrova V, Lagioia G, Gallucci T (2007) Managerial factors for evaluating eco-clustering approach. Ind Manag Data Syst 107(9):1335–1348

    Article  Google Scholar 

  7. Fan J, Han M, Wang J (2009) Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation. Pattern Recognit 42(11):2527–2540

    Article  Google Scholar 

  8. Fu H, Elmisery AM (2009) A new feature weighted fuzzy c-means clustering algorithm. Algarve, Portugal, pp 11–18

  9. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co., Inc., p 372

    Google Scholar 

  10. Graves D, Pedrycz W (2010) Kernel-based fuzzy clustering and fuzzy clustering: a comparative experimental study. Fuzzy Sets Syst 161(4):522–543

    Article  MathSciNet  Google Scholar 

  11. Güçdemir H, Selim H (2015) Integrating multi-criteria decision making and clustering for business customer segmentation. Ind Manag Data Syst 115(6):1022–1040

    Article  Google Scholar 

  12. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques (3rd ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA

    Google Scholar 

  13. Holland J (1975) Adaption in natural and artificial systems, JH Holland. University of Michigan Press, Ann Arbor

    Google Scholar 

  14. Huang JZ et al (2005) Automated variable weighting in k-means type clustering. IEEE Trans Pattern Anal Mach Intell 27(5):657–668

    Article  Google Scholar 

  15. Huber PJ (2011) Robust statistics. International Encyclopedia of statistical science. Springer, pp 1248–1251

    Google Scholar 

  16. Hung W-L, Yang M-S, Chen D-H (2008) Bootstrapping approach to feature-weight selection in fuzzy c-means algorithms with an application in color image segmentation. Pattern Recognit Lett 29(9):1317–1325

    Article  Google Scholar 

  17. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323

    Article  Google Scholar 

  18. Jimenez J, Cuevas F, Carpio J (2007) Genetic algorithms applied to clustering problem and data mining. In: Proceedings of the 7th WSEAS international conference on simulation, modelling and optimization. World Scientific and Engineering Academy and Society (WSEAS), World Scientific and Engineering Academy and Society (WSEAS), pp 219–224

  19. Kackar RN (1985) Off-line quality control, parameter design, and the Taguchi method. J Qual Technol 17:176–188

    Article  Google Scholar 

  20. Khotimah BK, Irhamni F, Sundarwati T (2016) A Genetic algorithm for optimized initial centers K-means clustering in SMEs. J Theor Appl Inf Technol 90(1):23

    Google Scholar 

  21. Krishna K, Murty MN (1999) Genetic K-means algorithm. IEEE Trans Syst Man Cybern Part B (Cybern) 29(3):433–439

    Article  Google Scholar 

  22. Kuo R, Nguyen TPQ (2019) Genetic intuitionistic weighted fuzzy k-modes algorithm for categorical data. Neurocomputing 330:116–126

    Article  Google Scholar 

  23. Kuo R, Zulvia FE (2018) Automatic clustering using an improved artificial bee colony optimization for customer segmentation. Knowl Inf Syst 57(2):331–357

    Article  Google Scholar 

  24. Leski JM (2016) Fuzzy c-ordered-means clustering. Fuzzy Sets Syst 286:114–133

    Article  MathSciNet  Google Scholar 

  25. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proc. Fifth Berkeley Symp. on Math. Statist. and Prob., pp 281–297

  26. Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recogn 33(9):1455–1465

    Article  Google Scholar 

  27. Michielssen E, Ranjithan S, Mittra R (1992) Optimal multilayer filter design using real coded genetic algorithms. IEE Proc J (Optoelectronics) 139(6):413–420

    Article  Google Scholar 

  28. Mohammadrezapour O, Kisi O, Pourahmad F (2018) Fuzzy c-means and K-means clustering with genetic algorithm for identification of homogeneous regions of groundwater quality. Neural Comput Appl 32:3763–3775

    Article  Google Scholar 

  29. Murthy CA, Chowdhury N (1996) In search of optimal clusters using genetic algorithms. Pattern Recognit Lett 17(8):825–832

    Article  Google Scholar 

  30. Pedrycz W, Rai P (2008) Collaborative clustering with the use of Fuzzy C-Means and its quantification. Fuzzy Sets Syst 159(18):2399–2427

    Article  MathSciNet  Google Scholar 

  31. Piernik M, Brzezinski D, Morzy T (2016) Clustering XML documents by patterns. Knowl Inf Syst 46(1):185–212

    Article  Google Scholar 

  32. Pizzuti C, Procopio N (2016) A K-means based genetic algorithm for data clustering. In: International joint conference SOCO’16-CISIS’16-ICEUTE’16, Springer, pp 211–222

  33. Sumathi S, Hamsapriya T, Surekha P (2008) Evolutionary intelligence: an introduction to theory and applications with Matlab. Springer, Berlin

    Google Scholar 

  34. Tagarelli A, Karypis G (2013) A segment-based approach to clustering multi-topic documents. Knowl Inf Syst 34(3):563–595

    Article  Google Scholar 

  35. Taguchi G (1986) Introduction to quality engineering: designing quality into products and processes. Asian productivity organization

    Google Scholar 

  36. Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson Education

    Google Scholar 

  37. Wang X, Wang Y, Wang L (2004) Improving fuzzy c-means clustering based on feature-weight learning. Pattern Recognit Lett 25(10):1123–1132

    Article  Google Scholar 

  38. Xi L, Zhang F (2019) An adaptive artificial-fish-swarm-inspired fuzzy C-means algorithm. Neural Comput Appl 32:16891–16899

    Article  Google Scholar 

  39. Xing H-J, Ha M-H (2014) Further improvements in feature-weighted fuzzy C-means. Inf Sci 267:1–15

    Article  MathSciNet  Google Scholar 

  40. Xu Z, Wu J (2010) Intuitionistic fuzzy C-means clustering algorithms. J Syst Eng Electron 21(4):580–590

    Article  MathSciNet  Google Scholar 

  41. Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans Syst Man Cybern 18(1):183–190

    Article  MathSciNet  Google Scholar 

  42. Yang C-L, Nguyen TPQ (2016) Constrained clustering method for class-based storage location assignment in warehouse. Ind Manag Data Syst 116(4):667–689

    Article  Google Scholar 

  43. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by The University of Danang, University of Science and Technology, code number of Project: T2020-02-16. This support is really appreciated.

Author information

Authors and Affiliations

  1. Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Kee-Lung Road, Taipei, 106, Taiwan

    R. J. Kuo

  2. Micron Memory Taiwan Co., Ltd., No. 369, Sec. 4, Sanfeng Rd., Houli Dist., Taichung City, 42152, Taiwan

    C. K. Chang

  3. Faculty of Project Management, The University of Danang, University of Science and Technology, 54 Nguyen Luong Bang, Danang, Vietnam

    Thi Phuong Quyen Nguyen

  4. Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA, 70808, USA

    T. W. Liao

Authors
  1. R. J. Kuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. K. Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thi Phuong Quyen Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T. W. Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thi Phuong Quyen Nguyen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuo, R.J., Chang, C.K., Nguyen, T.P.Q. et al. Application of genetic algorithm-based intuitionistic fuzzy weighted c-ordered-means algorithm to cluster analysis. Knowl Inf Syst 63, 1935–1959 (2021). https://doi.org/10.1007/s10115-021-01574-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-021-01574-4

Keywords

Search

Navigation