Advertisement

FPA clust: evaluation of the flower pollination algorithm for data clustering

  • J. SenthilnathEmail author
  • Sushant Kulkarni
  • S. Suresh
  • X. S. Yang
  • J. A. Benediktsson
Special Issue
  • 55 Downloads

Abstract

In this work, a standalone approach based on the flower pollination algorithm (FPA) is proposed for solving data clustering problems. The FPA is a nature-inspired algorithm simulating the behavior of flower pollination. The proposed approach is used to extract key information in terms of optimal cluster centers that are derived from training samples of the selected databases. These extracted cluster centers are then validated on test samples. Three datasets from the UCI machine learning data repository and an additional multi-spectral, real-time satellite image are chosen to illustrate the effectiveness and diversity of the proposed technique. The FPA performance is compared with the k-means, a popular clustering algorithm and metaheuristic algorithms, namely, the Genetic Algorithm, Particle Swarm Optimization, Cuckoo Search, Spider Monkey Optimization, Grey Wolf Optimization, Differential Evolution, Harmony Search and Bat Algorithm. The results are evaluated based on classification error percentage (CEP), time complexity and statistical significance. FPA has the lowest CEP for all four datasets and an average CEP of 28%, which is 5.5% lower than next best algorithm in that sense. The FPA is the second quickest algorithm to converge after HS algorithm. FPA also shows a higher level of statistical significance. Therefore, the obtained results show that the FPA efficiently clusters the data and performs better than the state-of-the-art methods.

Keywords

Data clustering Metaheuristic algorithm Flower pollination algorithm Multispectral dataset 

Notes

References

  1. 1.
    Mittal H, Saraswat M (2018) An image segmentation method using logarithmic kbest gravitational search algorithm based superpixel clustering. Evol Intell.  https://doi.org/10.1007/s12065-018-0192-y Google Scholar
  2. 2.
    Wu W, Xiong H, Shekhar S (2004) Clustering and information retrieval, vol 11. Springer, BostonGoogle Scholar
  3. 3.
    Berkhin P (2006) A survey of clustering data mining techniques. In: Kogan J, Nicholas C, Teboulle M (eds) Grouping multidimensional data. Springer, Berlin, pp 25–71Google Scholar
  4. 4.
    Senthilnath J, Kulkarni S, Benediktsson JA, Yang XS (2016) A novel approach for multispectral satellite image classification based on the bat algorithm. IEEE Geosci Remote Sens Lett 13(4):599–603Google Scholar
  5. 5.
    Xiao X, Dow ER, Eberhart R, Miled ZB, Oppelt RJ (2003) Gene clustering using self-organizing maps and particle swarm optimization. In: International proceedings of parallel and distributed processing symposium, pp 10Google Scholar
  6. 6.
    Hansen P, Jaumard B (1997) Cluster analysis and mathematical programming. Math Program 79(1–3):191–215MathSciNetzbMATHGoogle Scholar
  7. 7.
    Bandyopadhyay S, Maulik U (2002) An evolutionary technique based on K-Means algorithm for optimal clustering in RN. Inf Sci 146(1–4):221–237zbMATHGoogle Scholar
  8. 8.
    Senthilnath J, Omkar SN, Mani V (2011) Clustering using firefly algorithm: performance study. Swarm Evol Comput 1(3):164–171Google Scholar
  9. 9.
    Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471MathSciNetzbMATHGoogle Scholar
  10. 10.
    Jadon SS, Tiwari R, Sharma H, Bansal JC (2017) Hybrid artificial bee colony algorithm with differential evolution. Appl Soft Comput 58:11–24Google Scholar
  11. 11.
    Jadon SS, Bansal JC, Tiwari R, Sharma H (2018) Artificial bee colony algorithm with global and local neighborhoods. Int J Syst Assur Eng Manag 9(3):589–601Google Scholar
  12. 12.
    Mousavirad SJ, Ebrahimpour-Komleh H (2017) Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithms. Evol Intell 10(1):45–75Google Scholar
  13. 13.
    Yang X-S (2014) Swarm intelligence based algorithms: a critical analysis. Evol Intell 7(1):17–28Google Scholar
  14. 14.
    Krishna K, Murty MN (1999) Genetic K-means algorithm. IEEE Trans Syst Man Cybern Part B Cybern 29(3):433–439Google Scholar
  15. 15.
    Shelokar PS, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Anal Chim Acta 509(2):187–195Google Scholar
  16. 16.
    Falco ID, Cioppa AD, Tarantino E (2005) Evaluation of particle swarm optimization effectiveness in classification. In: Bloch I, Petrosino A, Tettamanzi AGB (eds) Fuzzy logic and applications. Springer, Berlin, pp 164–171Google Scholar
  17. 17.
    Aarts E, Krost J (1989) Simulated annealing and Boltzmann machines. Wiley, HobokenGoogle Scholar
  18. 18.
    Senthilnath J et al (2016) A novel harmony search-based approach for clustering problems. Int J Swarm Intell 2(1):66–85Google Scholar
  19. 19.
    Wahid F, Ghazali R (2018) Hybrid of firefly algorithm and pattern search for solving optimization problems. Evol Intell 12(1):1–10Google Scholar
  20. 20.
    Han X, Quan L, Xiong X, Almeter M, Xiang J, Lan Y (2017) A novel data clustering algorithm based on modified gravitational search algorithm. Eng Appl Artif Intell 61:1–7Google Scholar
  21. 21.
    Niknam T, Firouzi BB, Nayeripour M (2008) An efficient hybrid evolutionary algorithm for cluster analysis. World Appl Sci J 4(2):300–307Google Scholar
  22. 22.
    Kao Y-T, Zahara E, Kao I-W (2008) A hybridized approach to data clustering. Exp Syst Appl 34(3):1754–1762Google Scholar
  23. 23.
    Yang X-S (2012) Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional computation and natural computation. Springer, Berlin, pp 240–249Google Scholar
  24. 24.
    Wang R, Zhou Y, Qiao S, Huang K (2016) Flower pollination algorithm with bee pollinator for cluster analysis. Inf Process Lett 116(1):1–14Google Scholar
  25. 25.
    Łukasik S, Kowalski PA, Charytanowicz M, Kulczycki P (2016) Clustering using flower pollination algorithm and Calinski-Harabasz index. IEEE Congr Evolut Comput (CEC) 2016:2724–2728Google Scholar
  26. 26.
    Agarwal P, Mehta S (2016) Enhanced flower pollination algorithm on data clustering. Int J Comput Appl 38(2–3):144–155Google Scholar
  27. 27.
    Bansal JC, Sharma H, Jadon SS, Clerc M (2014) Spider monkey optimization algorithm for numerical optimization. Memetic Comput 6(1):31–47Google Scholar
  28. 28.
    Mirjalili SM, Lewis A (2014) Grey Wolf optimizer. Adv Eng Softw 69:46–61Google Scholar
  29. 29.
    Senthilnath J, Simha S, Thapa M (2018) BELMKN: Bayesian extreme learning machines Kohonen network. Algorithms 11(5):56MathSciNetGoogle Scholar
  30. 30.
    Senthilnath J, Das V, Omkar SN, Mani V (2013) Clustering using levy flight cuckoo search. In: Bansal JC, Singh P, Deep K, Pant M, Nagar A (eds) Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012), Springer, India, pp 65–75Google Scholar
  31. 31.
    Yang X-S, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237MathSciNetGoogle Scholar
  32. 32.
    Marinakis Y, Marinaki M, Doumpos M, Matsatsinis N, Zopounidis C (2008) A hybrid stochastic genetic–GRASP algorithm for clustering analysis. Oper Res 8(1):33–46zbMATHGoogle Scholar
  33. 33.
    Karaboga D, Ökdem S (2004) A simple and global optimization algorithm for engineering problems: differential evolution algorithmGoogle Scholar
  34. 34.
    Das S, Abraham A, Konar A (2008) Automatic clustering using an improved differential evolution algorithm. IEEE Trans Syst Man Cybern Part Syst Hum 38(1):218–237Google Scholar
  35. 35.
    Suresh S, Sundararajan N, Saratchandran P (2008) A sequential multi-category classifier using radial basis function networks. Neurocomputing 71(7–9):1345–1358Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Infocomm ResearchAgency for Science, Technology and Research (A*STAR)SingaporeSingapore
  2. 2.Tiger AnalyticsChennaiIndia
  3. 3.Department of Aerospace EngineeringIndian Institute of ScienceBangaloreIndia
  4. 4.School of Science and TechnologyMiddlesex UniversityLondonUK
  5. 5.Faculty of Electrical and Computer EngineeringUniversity of IcelandReykjavíkIceland

Personalised recommendations