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A chaotic sequence-guided Harris hawks optimizer for data clustering

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Abstract

Data clustering is one of the important techniques of data mining that is responsible for dividing N data objects into K clusters while minimizing the sum of intra-cluster distances and maximizing the sum of inter-cluster distances. Due to nonlinear objective function and complex search domain, optimization algorithms find difficulty during the search process. Recently, Harris hawks optimization (HHO) algorithm is proposed for solving global optimization problems. HHO has already proved its efficacy in solving a variety of complex problems. In this paper, a chaotic sequence-guided HHO (CHHO) has been proposed for data clustering. The performance of the proposed approach is compared against six state-of-the-art algorithms using 12 benchmark datasets of the UCI machine learning repository. Various comparative performance analysis and statistical tests have justified the effectiveness and competitiveness of the suggested approach.

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References

  1. Panov P, Džeroski S, Soldatova L (2008) Ontodm: An ontology of data mining. In: 2008 IEEE international conference on data mining workshops. IEEE, pp 752–760

  2. Berikov V (2014) Weighted ensemble of algorithms for complex data clustering. Pattern Recognit Lett 38:99–106

    Article  Google Scholar 

  3. Zhou HF, Li J, Li JH, Zhang FC, Cui YA (2017) A graph clustering method for community detection in complex networks. Physica A Stat Mech Appl 469:551–562

    Article  Google Scholar 

  4. Katarya R, Verma OP (2017) An effective web page recommender system with fuzzy c-mean clustering. Multimed Tools Appl 76(20):21481–21496

    Article  Google Scholar 

  5. Deng J, Hu JL, Chi H, Wu J (2010) An improved fuzzy clustering method for text mining. In: 2010 Second international conference on networks security, wireless communications and trusted computing. IEEE, vol 1, pp 65–69

  6. Jumb V, Sohani M, Shrivas A (2014) Color image segmentation using k-means clustering and Otsu’s adaptive thresholding. Int J Innov Technol Explor Eng (IJITEE) 3(9):72–76

    Google Scholar 

  7. Lee AJT, Lin M-C, Kao R-T, Chen K-T (2010) An effective clustering approach to stock market prediction. In: PACIS. pp 54

  8. Pal SK, Wang PP (2017) Genetic algorithms for pattern recognition. CRC Press, Boca Raton

    Book  MATH  Google Scholar 

  9. Price KV (2013) Differential evolution. In: Zelinka I, Snasel V, Abraham A (eds) Handbook of optimization. Springer, Berlin, pp 187–214

    Chapter  Google Scholar 

  10. Kennedy J (2010) Particle swarm optimization. In: de Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Berlin, pp 760–766

    Google Scholar 

  11. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  12. Maulik U, Bandyopadhyay S, Mukhopadhyay A (2011) Multiobjective genetic algorithms for clustering: applications in data mining and bioinformatics. Springer, Berlin

    Book  MATH  Google Scholar 

  13. Maulik U, Saha I (2010) Automatic fuzzy clustering using modified differential evolution for image classification. IEEE Trans Geosci Remote Sens 48(9):3503–3510

    Article  Google Scholar 

  14. Das S, Sil S (2010) Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm. Inf Sci 180(8):1237–1256

    Article  MathSciNet  Google Scholar 

  15. Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 16:1–18

    Article  Google Scholar 

  16. Zhang C, Ouyang D, Ning J (2010) An artificial bee colony approach for clustering. Expert Syst Appl 37(7):4761–4767

    Article  Google Scholar 

  17. Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11(1):652–657

    Article  Google Scholar 

  18. Rana S, Jasola S, Kumar R (2011) A review on particle swarm optimization algorithms and their applications to data clustering. Artif Intell Rev 35(3):211–222

    Article  Google Scholar 

  19. Esmin AAA, Coelho RA, Matwin S (2015) A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artif Intell Rev 44(1):23–45

    Article  Google Scholar 

  20. Tripathi AK, Sharma K, Bala M (2018) A novel clustering method using enhanced grey wolf optimizer and mapreduce. Big Data Res 14:93–100

    Article  Google Scholar 

  21. Kaur G, Arora S (2018) Chaotic whale optimization algorithm. J Comput Des Eng 5(3):275–284

    Google Scholar 

  22. Chuang L-Y, Hsiao C-J, Yang C-H (2011) Chaotic particle swarm optimization for data clustering. Expert Syst Appl 38(12):14555–14563

    Article  Google Scholar 

  23. Li C, Zhou J, Kou P, Xiao J (2012) A novel chaotic particle swarm optimization based fuzzy clustering algorithm. Neurocomputing 83:98–109

    Article  Google Scholar 

  24. Wan M, Wang C, Li L, Yang Y (2012) Chaotic ant swarm approach for data clustering. Appl Soft Comput 12(8):2387–2393

    Article  Google Scholar 

  25. Jamshidi MB, Jamshidi M, Rostami S (2017) An intelligent approach for nonlinear system identification of a Li-ion battery. In: 2017 IEEE 2nd international conference on automatic control and intelligent systems (I2CACIS). IEEE, pp 98–103

  26. Mohammad BJ, Neda A (2017) Neuro-fuzzy system identification for remaining useful life of electrolytic capacitors. In: 2017 2nd International conference on system reliability and safety (ICSRS). IEEE, pp 227–231

  27. Jamshidi MB, Gorjiankhanzad M, Lalbakhsh A, Roshani S (2019) A novel multiobjective approach for detecting money laundering with a neuro-fuzzy technique. In: 2019 IEEE 16th international conference on networking, sensing and control (ICNSC). IEEE, pp 454–458

  28. Jamshidi MB, Alibeigi N, Lalbakhsh A, Roshani S (2019) An anfis approach to modeling a small satellite power source of NASA. In: 2019 IEEE 16th international conference on networking, sensing and control (ICNSC). IEEE, pp 459–464

  29. Lalbakhsh A, Afzal MU, Esselle K (2016) Simulation-driven particle swarm optimization of spatial phase shifters. In: 2016 International conference on electromagnetics in advanced applications (ICEAA). IEEE, pp 428–430

  30. Lalbakhsh P, Zaeri B, Lalbakhsh A (2013) An improved model of ant colony optimization using a novel pheromone update strategy. IEICE Trans Inf Syst 96(11):2309–2318

    Article  Google Scholar 

  31. Lalbakhsh P, Zaeri B, Lalbakhsh A, Fesharaki MN (2010) Antnet with reward-penalty reinforcement learning. In: 2010 2nd International conference on computational intelligence, communication systems and networks. IEEE, pp 17–21

  32. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872

    Article  Google Scholar 

  33. Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver press, London

    Google Scholar 

  34. Ewees AA, El Aziz MA, Hassanien AE (2017) Chaotic multi-verse optimizer-based feature selection. In: Mirjalili S, Dong JS, Lewis A (eds) Neural computing and applications. Springer, Berlin, pp 1–16

    Google Scholar 

  35. El-Shorbagy MA, Mousa AA, Nasr SM (2016) A chaos-based evolutionary algorithm for general nonlinear programming problems. Chaos Solitons Fractals 85:8–21

    Article  MathSciNet  MATH  Google Scholar 

  36. Ott E (2002) Chaos in dynamical systems. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  37. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715–734

    Article  Google Scholar 

  38. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  39. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  40. Mirjalili S (2016) Sca: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133

    Article  Google Scholar 

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  1. Department of Computer Engineering and Applications, GLA University, Mathura, India

    Tribhuvan Singh

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  1. Tribhuvan Singh
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Correspondence to Tribhuvan Singh.

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Singh, T. A chaotic sequence-guided Harris hawks optimizer for data clustering. Neural Comput & Applic 32, 17789–17803 (2020). https://doi.org/10.1007/s00521-020-04951-2

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