Skip to main content
SpringerLink
Log in

A Clustering Based Classification Approach Based on Modified Cuckoo Search Algorithm

  • MATHEMATICAL METHOD IN PATTERN RECOGNITION
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

Cuckoo Search Algorithm (CSA) is one of the new swarm intelligence based optimization algorithms, which has shown an effective performance on many optimization problems. However, the effectiveness of CSA significantly depends on the exploration and exploitation potential and it may also possible to increase its efficiency when solving complex optimization problems. In this study, some mechanisms have been employed on CSA to increase its efficiency such as use of global best and individual best solutions to guide the other solutions, self-adaption techniques for parameters and so on. The modified CSA (i.e., MCSA) is successfully employed in clustering based classification domain. The experimental results and execution time prove its effectiveness over existing modified CSAs and other employed swarm intelligence algorithms. The proposed clustering model is also employed in color histopathological image segmentation domain and provides effective result.

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. M. R. Anderberg, Cluster Analysis for Application (Academic Press, New York, 1973).

    MATH  Google Scholar 

  2. J. A. Hartigan, Clustering Algorithms (Wiley, New York, 1975).

    MATH  Google Scholar 

  3. P. A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach (Prentice-Hall, London, 1982).

    MATH  Google Scholar 

  4. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data (Prentice-Hall, Englewood Cliffs, 1988).

    MATH  Google Scholar 

  5. Y. Leung, J.-S. Zhang, and Z.-B. Xu, “Clustering by scale-space filtering,” IEEE Trans. Pattern Anal. Mach. Intell. 22 (12), 1396–1410 (2000).

    Article  Google Scholar 

  6. J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symposium on Mathematics and Statistical Probability (Berkeley, CA, USA, 1967), Vol. 1 (Univ. of Calif. Press, 1967), pp. 281–297.

  7. E. Falkenauer, Genetic Algorithms and Grouping Problems (Wiley, Chichester, UK, 1998).

    MATH  Google Scholar 

  8. S. Paterlini and T. Minerva, “Evolutionary approaches for cluster analysis,” in Soft Computing Applications, Ed. by A. Bonarini, F. Masulli, and G. Pasi, Advances in Soft Computing (Springer, Physica, Heidelberg, 2003), Vol. 18, pp. 167–178.

  9. C.-H. Tsang and S. Kwong, “Ant colony clustering and feature extraction for anomaly intrusion detection,” in Swarm Intelligence in Data Mining, Ed. by A. Abraham, C. Grosan, and V. Ramos, Studies in Computational Intelligence (Springer, Berlin, Heidelberg, 2006), Vol. 34, pp. 101–123.

  10. R. Younsi and W. Wang, “A new artificial immune system algorithm for clustering,” in Intelligent Data Engineering and Automated Learning – IDEAL 2004, Ed. by Z. R. Yang, H. Yin, and R. M. Everson, Lecture Notes in Computer Science (Springer, Berlin, Heidelberg, 2004), Vol. 3177, pp. 58–64.

    Google Scholar 

  11. P. S. Shelokar, V. K. Jayaraman, and B. D. Kulkarni, “An ant colony approach for clustering,” Anal. Chim. Acta 509 (2), 187–195 (2004).

    Article  Google Scholar 

  12. S. Paterlini and T. Krink, “Differential evolution and particle swarm optimisation in partitional clustering,” Comput. Stat. Data Anal. 50 (5), 1220–1247 (2006).

    Article  MathSciNet  MATH  Google Scholar 

  13. Y. Kao and K. Cheng, “An ACO-based clustering algorithm,” in Ant Colony Optimization and Swarm Intelligence, ANTS 2006, Ed. by M. Dorigo, Lecture Notes in Computer Science (Springer, Berlin, Heidelberg, 2006), Vol. 4150, pp. 340–347.

    Google Scholar 

  14. M. Omran, A. Engelbrecht, and A. Salman, “Particle swarm optimization method for image clustering,” Int. J. Pattern Recogn. Artif. Intell. 19 (3), 297–322 (2005).

    Article  Google Scholar 

  15. S. J. Nanda and G. Panda, “A survey on nature inspired metaheuristic algorithms for partitional clustering,” Swarm Evol. Comput. 16, 1–18 (2014).

    Article  Google Scholar 

  16. T. Niknam, B. Amiri, J. Olamaei, and A. Arefi, “An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering,” J. Zhejiang Univ. Sci. A 10 (4), 512–519 (2009).

    Article  MATH  Google Scholar 

  17. T. Niknam, E. Taherian Fard, N. Pourjafarian, and A. R. Rousta, “An efficient hybrid algorithm based on modified imperialist competitive algorithm and k-means for data clustering,” Eng. Appl. Artif. Intell. 24 (2), 306–317 (2011).

    Article  Google Scholar 

  18. T. Niknam and B. Amiri, “An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis,” Appl. Soft Comput. 10 (1), 183–197 (2010).

    Article  Google Scholar 

  19. I. De Falco, A. D. Cioppa, and E. Tarantino, “Facing classification problems with particle swarm optimization,” Appl. Soft Comput. 7 (3), 652–658 (2007).

    Article  Google Scholar 

  20. F. V. Jensen, An Introduction to Bayesian Networks (UCL Press, London, 1996).

    Google Scholar 

  21. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representation by back propagation errors,” Nature 323, 533–536 (1986).

    Article  MATH  Google Scholar 

  22. M. H. Hassoun, Fundamentals of Artificial Neural Networks (MIT Press, Cambridge, MA, 1995).

    MATH  Google Scholar 

  23. J. G. Cleary and L. E. Trigg, “K*: An instance-based learner using an entropic distance measure,” in Proc. 12th Int. Conf. on Machine Learning, Tahoe City, CA, 1995, Machine Learning Proceedings 1995 (Morgan Kaufmann, San Francisco, 1995), pp. 108–114.

  24. L. Breiman, “Bagging predictors,” Mach. Learn. 24 (2), 123–140 (1996).

    MATH  Google Scholar 

  25. G. I. Webb, “Multi boosting: A technique for combining boosting and wagging,” Mach. Learn. 40 (2), 159–196 (2000).

    Article  Google Scholar 

  26. R. Kohavi, “Scaling up the accuracy of Naive–Bayes classifiers: A decision tree hybrid,” in Proc. Second Int. Conf. on Knowledge Discovery and Data Mining (KDD’96) (Portland, OR, USA, 1996) (AAAI Press, 1996), pp. 202–207.

  27. P. Compton and R. Jansen, “Knowledge in context: A strategy for expert system maintenance,” in AI’88, Proc. 2nd Australian Joint Artificial Intelligence Conference, Ed. by C. J. Barter and M. J. Brooks, Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence) (Springer, Berlin, Heidelberg, 1990), Vol. 406, pp. 292–306.

  28. G. Demiröz and H. A. Güvenir, “Classification by voting feature intervals,” in Machine Learning: ECML-97, Proc. 9th European Conf. on Machine Learning, Ed. by M. van Someren and G. Widmer, Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence) (Springer, Berlin, Heidelberg, 1997), Vol. 1224, pp. 85–92.

  29. D. Karaboga and C. Ozturk, “A novel cluster approach: Artificial Bee Colony (ABC) algorithm,” Appl. Soft Comput. 11 (1), 652–657 (2010).

    Article  Google Scholar 

  30. I. B. Saida, K. Nadjet, and B. Omar, “A new algorithm for data clustering based on cuckoo search optimization,” in Genetic and Evolutionary Computing, Ed. by J. S. Pan, P. Krömer, and V. Snášel, Advances in Intelligent Systems and Computing (Springer, Cham, 2014), Vol. 238, pp. 55–64.

  31. J. Senthilnath, S. N. Omkar, and V. Mani, “Clustering using firefly algorithm: Performance study,” Swarm Evol. Comput. 1 (3), 164–171 (2011).

    Article  Google Scholar 

  32. J. Senthilnath, Sushant Kulkarni, J. A. Benediktsson, and X. S. Yang, “A novel approach for multispectral satellite image classification based on the bat algorithm,” IEEE Geosci. Remote Sens. Lett. 13 (4), 599–603 (2016).

    Article  Google Scholar 

  33. J. Zhao, X. Lei, Z. Wu, and Y. Tan, “Clustering using improved cuckoo search algorithm,” in Advances in Swarm Intelligence, ICSI 2014, Part I, Ed. by Y. Tan, Y. Shi, and C. A. C. Coello, Lecture Notes in Computer Science (Springer, Cham, 2014), Vol. 8794, pp. 479–488.

    Google Scholar 

  34. C. Cobos, H. Muñoz-Collazos, R. Urbano-Muñoz, M. Mendoza, E. León, and E. Herrera-Viedma, “Clustering of web search results based on the cuckoo search algorithm and Balanced Bayesian Information Criterion,” Inf. Sci. 281, 248–264 (2014).

    Article  Google Scholar 

  35. S. Goel, A. Sharma, and P. Bedi, “Novel approaches for classification based on Cuckoo Search Strategy,” Int. J. Hybrid Intell. Syst. 10 (3), 107–116 (2013).

    Article  Google Scholar 

  36. K. G. Dhal, Md. I. Quraishi, and S. Das, “An improved cuckoo search based optimal ranged brightness preserved histogram equalization and contrast stretching method,” Int. J. Swarm Intell. Res. 8 (1), 1–29 (2017).

    Article  Google Scholar 

  37. C. L. Blake and C. J. Merz, University of California at Irvine Repository of Machine Learning Databases (1998). http://www.ics.uci.edu/mlearn/MLRepository.html

  38. X.-S. Yang, and S. Deb, “Cuckoo Search via lévy flight,” in Proc. 2009 World Congress on Nature and Biologically Inspired Computing (NaBIC 2009) (Coimbatore, India, 2009), IEEE, pp. 210–214.

  39. J. Kennedy, “Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance,” in Proc. 1999 Congress on Evolutionary Computation (CEC99) (Washington, USA, 1999), IEEE, Vol. 3, pp. 1931–1938.

  40. H. Wang, Z. Wu, and S. Rahnamayan, “Particle swarm optimisation with simple and efficient neighbourhood search strategies,” Int. J. Innovative Comput. Appl. 3 (2), 97–104 (2011).

    Article  Google Scholar 

  41. S. Das, A. Abraham, U. K. Chakraborty, and A. Konar, “Differential evolution using a neighborhood-based mutation operator,” IEEE Trans. Evol. Comput. 13 (3), 526–553 (2009).

    Article  Google Scholar 

  42. H. Wang, Z. Cui, H. Sun, S. Rahnamayan, and X.‑S. Yang, “Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism,” Soft Comput. 21 (18), 5325–5339 (2017).

    Article  Google Scholar 

  43. L. dos Santos Coelho and V. C. Mariani, “A novel particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch,” Chaos, Solitons Fractals 39 (2), 510–518 (2009).

    Article  Google Scholar 

  44. R. Sheikholeslami and A. Kaveh, “A survey of chaos embedded meta-heuristic algorithms,” Int. J. Optim. Civil. Eng. 3 (4), 617–633 (2013).

    Google Scholar 

  45. L. dos Santos Coelho and V. C. Mariani, “Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization,” Expert Syst. Appl. 34 (3), 1905–1913 (2008).

    Article  Google Scholar 

  46. A. R. Jordehi, “A chaotic-based big bang–big crunch algorithm for solving global optimisation problems,” Neural Comput. Appl. 25 (6), 1329–1335 (2014).

    Article  Google Scholar 

  47. C. Choi and J.-J. Lee, “Chaotic local search algorithm,” Artif. Life Rob. 2 (1), 41–47 (1998).

    Article  Google Scholar 

  48. J. C. Bansal, P. K. Singh, M. Saraswat, A. Verma, S. S. Jadon, and A. Abraham, “Inertia Weight strategies in Particle Swarm Optimization,” in Proc. 2011 Third World Congress on Nature and Biologically Inspired Computing (Salamanca, Spain, 2011), pp. 633–640 (2011).

  49. R. Caponetto, L. Fortuna, S. Fazzino, and M. G. Xibilia, “Chaotic sequences to improve the performance of evolutionary algorithms,” IEEE Trans. Evol. Comput. 7 (3), 289–304 (2003).

    Article  Google Scholar 

  50. M. Jamil and H. J. Zepernick, “Lévy flights and global optimization,” in Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, Ed. by X.‑S. Yang, R. Xiao, et al. (Elsevier, London, 2013), pp. 49–72. https://doi.org/10.1016/B978-0-12-405163-8.00003-X

  51. K. G. Dhal, Md. I. Quraishi, and S. Das, “Development of firefly algorithm via chaotic sequence and population diversity to enhance the image contrast,” Nat. Comput. 15 (2), 307–318 (2016).

    Article  MathSciNet  MATH  Google Scholar 

  52. X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Trans. Evol. Comput. 3 (2), 82–102 (1999).

    Article  Google Scholar 

  53. R. Wang, Y. Zhou, C. Zhao, and H. Wu, “A hybrid flower pollination algorithm based modified randomized location for multithreshold medical image segmentation,” Bio-Med. Mater. Eng. 26 (s1), S1345–S1351 (2015).

    Article  Google Scholar 

  54. K. G. Dhal and S. Das, S, “Diversity conserved chaotic Artificial Bee Colony algorithm based brightness preserved histogram equalization and contrast stretching method,” Int. J. Nat. Comput. Res. (IJNCR) 5 (4), 45–73 (2015).

    Article  Google Scholar 

  55. I. Saha, U. Maulik, and D. Plewczynski, “A new multi-objective technique for differential fuzzy clustering,” Appl. Soft Comput. 11 (2), 2765–2776 (2011).

    Article  Google Scholar 

  56. N. Jardine, and R. Sibson, Mathematical Taxonomy (Wiley, New York, 1971).

    MATH  Google Scholar 

  57. K. Y. Yeung, and W. L. Ruzzo, “Principal component analysis for clustering gene expression data,” Bioinf. 17 (9), 763–774 (2001).

    Article  Google Scholar 

  58. S. Park, D. Sargent, R. Lieberman, and U. Gustafsson, “Domain-specific image analysis for cervical neoplasia detection based on conditional random fields,” IEEE Trans. Med. Imag. 30 (3), 867–878 (2011).

    Article  Google Scholar 

  59. Y. Xu, J.-Y. Zhu, E. I.-C. Chang, M. Laid, and Z. Tu, “Weakly supervised histopathology cancer image segmentation and classification,” Med. Image Anal. 18 (3), 591–604 (2014).

    Article  Google Scholar 

  60. M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhush, N. M. Rajpoot, and B. Yener, “Histopathological image analysis: A review,” IEEE Rev. Biomed. Eng. 2, 147–171 (2009).

    Article  Google Scholar 

  61. M. M. R. Krishnan, P. Shah, C. Chakraborty, and A. K. Ray, “Statistical analysis of textural features for improved classification of oral histopathological images,” J. Med. Syst. 36 (2), 865–881 (2012).

    Article  Google Scholar 

  62. Z. Pan (Department of Pathology, University of Colorado Denver), Enjoy Pathology at http://www.enjoypath.com/

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India

    Krishna Gopal Dhal

  2. Department of Information Technology, Kalyani Government Engineering College, Kalyani, Nadia, India

    Arunita Das

  3. Skybound Digital LLC Pvt. Ltd., Kolkata, West Bengal, India

    Swarnajit Ray

  4. Department of Engineering and Technological Studies, University of Kalyani, Kalyani, Nadia, India

    Sanjoy Das

Authors
  1. Krishna Gopal Dhal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arunita Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Swarnajit Ray
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanjoy Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Krishna Gopal Dhal, Arunita Das, Swarnajit Ray or Sanjoy Das.

Additional information

Mr. Krishna Gopal Dhal completed his B.Tech. and M.Tech. from Kalyani Government Engineering College, West Bengal, India. Currently he is working as Assistant Professor in Dept. of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India. His research interests are digital image processing, nature-inspired optimization algorithms, and medical imaging.

Dr. Sanjoy Das completed his B.E. from Regional Engineering College, Durgapur, M.E. from Bengal Engineering College (Deemed Univ.), Howrah, PhD from Bengal Engineering and Science University, Shibpur. Currently he is working as Associate Professor in Department of Engineering and Technological Studies, University of Kalyani, Nadia, West Bengal, India. His research interests are tribology and optimization techniques.

Ms. Arunita Das completed her B.Sc. and M.Sc. in Computer Science from Vidyasagar University, Paschim Medinipur, West Bengal, India. She is the recipient of the University Silver Medal two times for achieving second position in B.Sc. and M.Sc. courses. Currently she is pursuing her M.Tech. in the dept. of Information Technology, Kalyani Government Engineering College, West Bengal, India. Her research interests are medical image processing and nature-inspired optimization algorithms.

Mr. Swarnajit Ray completed his B.Tech. from the Narula Institute of Technology and M.Tech. from the Kalyani Government Engineering College, West Bengal, India. His research interests are Medical Image processing and Nature-Inspired Optimization Algorithms. Currently, he is senior web and app developer in Skybound Digital LLC, Kolkata, West Bengal, India.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dhal, K.G., Das, A., Ray, S. et al. A Clustering Based Classification Approach Based on Modified Cuckoo Search Algorithm. Pattern Recognit. Image Anal. 29, 344–359 (2019). https://doi.org/10.1134/S1054661819030052

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1054661819030052

Keywords:

Search

Navigation