A string grammar possibilistic-fuzzy C-medians


In the context of syntactic pattern recognition, we adopt the fuzzy clustering approach to classify the syntactic pattern. A syntactic pattern can be described using a string grammar. Fuzzy clustering has been shown to have better performance than hard clustering. Previously, to improve the string grammar hard C-means, we introduced a string grammar fuzzy C-medians and string grammar fuzzy-possibilistic C-medians algorithm. However, both algorithms have their own problem. Thus, in this paper, we develop a string grammar possibilistic-fuzzy C-medians algorithm. The experiments on four real data sets show that string grammar possibilistic-fuzzy C-medians has better performance than string grammar hard C-means, string grammar fuzzy C-medians, and string grammar fuzzy-possibilistic C-medians. We claim that the proposed string grammar possibilistic-fuzzy C-medians is better than the other string grammar clustering algorithms.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. Abdi A, Idris N, Ahmad Z (2016) QAPD: an ontology-based question answering system in the physics domain. Soft Comput 22:213–230

    Article  Google Scholar 

  2. Attalla E, Siy P (2005) Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching. Pattern Recogn 38:2229–2241

    Article  Google Scholar 

  3. Ayad LAK, Barton C, Pissis SP (2017) A faster and more accurate heuristic for cyclic edit distance computation. Pattern Recogn Lett 88:81–87

    Article  Google Scholar 

  4. Balázs K, Fekete RB (2009) Boosting products of base classifiers. In: Proceedings of the 26th annual international conference on machine learning

  5. Bezdek JC (1974) Cluster validity with fuzzy sets. J Cybern 3(3):58–72

    MathSciNet  Article  MATH  Google Scholar 

  6. Bezdek JC (1975) Mathematical models for systematics and taxonomy. In: Estabrook G (ed) Proceedings of 8th international conference on numerical taxonomy, Freeman, San Franscisco, CA, pp 143–166

  7. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

  8. Bezdek JC, Keller J, Krishnapuram R, Pal NR (1999) Fuzzy models and algorithms for pattern recognition and image processing. Kluwer Academic Publishers, Norwell

    Google Scholar 

  9. Cha SH, Shin YC, Srihari SN (1999) Approximate stroke sequence matching algorithm for character recognition and analysis. In: 5th international conference on document analysis and recognition, pp 53–56

  10. Chanda P, Auephanwiriyakul S, Theera-Umpon N (2012) Thai sign language translation system using upright speed-up robust feature and C-means clustering. In: IEEE international conference on fuzzy systems in part of the 2012 IEEE world congress on computational intelligence (WCCI 2012)

  11. Deng J, Hu J, Chi H, Wu J (2010) An Improved Fuzzy Clustering Method for Text Mining. In: Second international conference on networks security, wireless communications and trusted computing

  12. Fating K, Ghotkar A (2014) Performance analysis of chain code descriptor for hand shape classification. Int J Comput Graph Anim (IJCGA) 4(2):9

    Article  Google Scholar 

  13. Fu KS (1982) syntactic pattern recognition and applications. Prentice-Hall, Upper Saddle River

    Google Scholar 

  14. Fu KS, Lu SY (1977) A clustering procedure for syntactic patterns. IEEE Trans Syst Man Cybern 7:734–742

    MathSciNet  Article  MATH  Google Scholar 

  15. Gomez-Adorno H, Sidorov G, Pinto D, Vilarino D, Gelbukh A (2016) Automatic authorship detection using textual patterns extracted from integrated syntactic graphs. Sensors (Switzerland) 16(9):374

    Article  Google Scholar 

  16. Gonzalez RC, Thomason MG (1978) Syntactic pattern recognition an introduction. Addison Wesley Publishing Company, Boston, pp 12–13

    Google Scholar 

  17. Gonzalez RC, Woods RE, Eddins SL (2004) Digital image processing using MATLAB. Pearson Prentice Hall, Upper Saddle River

    Google Scholar 

  18. Granum E, Thomason MG (1990) Automatically inferred Markov network models for classification of chromosomal band pattern structures. Cytometry 11:26–39

    Article  Google Scholar 

  19. Granum E, Thomason MG, Gregor J (1989) On the use of automatically inferred Markov networks for chromosome analysis. In: Automation of cytogenetics, pp 233–251

  20. He J, Song T, Peng W, Sheng Q, Song J (2016) Automatic acquisition of matching patterns for pattern-based parsing on specific Chinese text. In: 2016 IEEE/WIC/ACM international conference on web intelligence workshops (WIW 2016), pp 17–20

  21. Higuera C, Casacuberta F (2000) The topology of strings: two np-complete problems. Theoret Comput Sci 230:39–48

    MathSciNet  Article  MATH  Google Scholar 

  22. Juan A, Vidal E (2000) On the use of normalized edit distances and an efficient k-NN search technique (k-AESA) for fast and accurate string classification. In: 2000 Proceedings of 15th international conference on pattern recognition, Barcelona pp 676–679

  23. Kersten PR (1995) The fuzzy median and fuzzy mad. In: Proceedings of ISUMA/NAFIPS, College Park, MD, pp 85–88

  24. Kersten PR (1999) Fuzzy order statistics and their application to fuzzy clustering. IEEE Trans Fuzzy Syst 7:708–712

    Article  Google Scholar 

  25. Keysers D, Dahmen J, Theiner T, Ney H (2000) Experiments with an extended tangent distance. In: 15th international conference on pattern recognition, vol 2, pp 38–42

  26. Keysers D, Deselaers T, Gollan C, Ney H (2007) Deformation models for image recognition. IEEE Trans Pattern Anal Mach Intell 29(8):422–1435

    Article  Google Scholar 

  27. Kim S-H, Cho H-G (2017) Position-restricted approximate string matching with metric hamming distance. In: 2017 IEEE international conference on big data and smart computing (BigComp), pp 108–114

  28. Klomsae A, Auephanwiriyakul S, Theera-Umpon N (2015) A novel string grammar fuzzy C-medians. In: IEEE international conference on fuzzy systems, Istanbul

  29. Klomsae A, Auephanwiriyakul S, Theera-Umpon N (2017) A string grammar fuzzy possibilistic C-medians. Appl Soft Comput 57:684–695

    Article  Google Scholar 

  30. Kohonen T (1985) Median strings. Pattern Recogn Lett 3:309–313

    Article  Google Scholar 

  31. Krishnapuram R, Keller J (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1(2):98–110

    Article  Google Scholar 

  32. Lundsteen C, Phillip J, Granum E (1980) Quantitative andlysis of 6985 digitized trysin {G}-banded human metaphase chromosomes. Clin Genet 18:355–370

    Article  Google Scholar 

  33. Martinez CD, Juan A, Casacuberta F (2000) Use of median string for classification. In: Proceedings of 15th international conference on pattern recognition, vol 2, pp 903–906

  34. McMahon T, Oommen BJ (2017) Enhancing English–Japanese translation using syntactic pattern recognition methods. In: 10th international conference on computer recognition systems (CORES 2017)

  35. Mohanty N, Rath TM, Lee A, Manmatha R (2005) Learning shapes for image classification and retrieval. In: Image and video retrieval 4th international conference, Singapore

  36. Neuhaus M, Bunke H (2006) Edit distance based kernel functions for structural pattern classification. Pattern Recogn 39:1852–1863

    Article  MATH  Google Scholar 

  37. Pal NR, Bezdek JC (1995) On cluster validity for the fuzzy C-means model. IEEE Trans Fuzzy Syst 3(3):370–379

    Article  Google Scholar 

  38. Pal R, Pal K, Bezdek JC (1997) A mixed C-means clustering model. In: IEEE international conference on fuzzy systems, Spain, pp 11–21

  39. Pal R, Pal K, Keller J, Bezdek JC (2005) A possibilistic fuzzy C-means clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517–529

    Article  Google Scholar 

  40. Patil N, Toshniwal D, Garg K (2012) Method of Fuzzy Matching Feature Extraction and Clustering Genome Data. In: IACSIT Hong Kong conferences

  41. Rabbani M, Alam KMR, Islam M, Morimoto Y (2015) A new stroke matching based approach to recognize Bangla handwritten text. In: 2015 18th international conference on computer and information technology (ICCIT 2015), pp 501–506

  42. Sebastian T, Klein P, Kimia B (2003) On aligning curves. IEEE Trans Pattern Anal Mach Intell 25(1):116–125

    Article  Google Scholar 

  43. Seewald AK (2012) On the brittleness of handwritten digit recognition models. International scholarly research notices machine vision

  44. Super BJ (2004) Learning chance probability functions for shape retrieval or classification. In: IEEE workshop on learning in computer vision and pattern recognition (at CVPR), Washington DC

  45. Vidal E, Castro MJ (1997) Classification of banded chromosomes using error-correcting grammatical inference (ECGI) and multilayer perceptron (MLP). In: VII national symposium on pattern recognition and image (SNRFAI), vol 1, Barcelona, pp 31–36

  46. Xie XL, Beni GA (1991) Validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 3(8):841–846

    Article  Google Scholar 

  47. Yang X, Oknar-Tezel SK, Latecki LJ (2009) Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval. In: IEEE conference on computer vision and pattern recognition, pp 357–364

  48. Yeh MC, Cheng KT (2008) A string matching approach for visual retrieval and classification. In: Proceeding of the ACM SIGMOD

  49. Yildiz T, Diri B, Yildirim S (2016) Acquisition of Turkish meronym based on classification of patterns. Pattern Anal Appl 19(2):495–507

    MathSciNet  Article  Google Scholar 

  50. Zhang S, Wang H (2010) Applying edit distance to hand language video. In: 2010 international conference on computer, mechatronics, control and electronic engineering (CMCE)

  51. Zhang C, Tang J, Luo B (2006) Shape edit distance on contour based shapes. In: Proceedings of the sixth international conference on intelligent systems design and applications (ISDA’06)

Download references


The authors would like to thank Thailand Research Fund and ChiangMai University under the Royal Golden Jubilee Ph.D. Program (Grant no. PHD/0044/2555) for financial support.

Author information



Corresponding author

Correspondence to Sansanee Auephanwiriyakul.

Ethics declarations

Conflict of interest

The authors of the paper do have any conflict of interest with any companies or institutions.

Human and animal rights statement

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher’s Note

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

Communicated by V. Loia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Klomsae, A., Auephanwiriyakul, S. & Theera-Umpon, N. A string grammar possibilistic-fuzzy C-medians. Soft Comput 23, 7637–7653 (2019). https://doi.org/10.1007/s00500-018-3392-6

Download citation


  • Fuzzy median
  • String grammar possibilistic-fuzzy c-medians
  • Levenshtein distance
  • Syntactic pattern recognition