Abstract
Data clustering plays a crucial role in the analysis of information collected from a variety of domains. Researchers developed many classical and mathematical algorithms to solve real-life problems, but due to the inherent property of these algorithms, they prematurely converge and fall to local optima. A further pattern of data in terms of shape, size, and distribution has a significant effect on the exploitation and exploration characteristic of algorithms which draw attention to many researchers. This work attempts to solve this problem by proposing an LNSMO local neighbour spider monkey optimization algorithm for data clustering. In the proposed algorithm Local Leader Phase of the spider monkey optimization algorithm is improved with its neighbour solution. Further to enhance the global search global leader phase of spider monkey optimization is improved with a chaotic operator. The performance of LNSMO is compared with eleven real-life datasets with five well-known Meta-heuristic algorithms in terms of a sum of within-cluster distance and convergence speed. It is further compared with recently developed hybrid meta-heuristic algorithms. Experimental result demonstrates that the proposed algorithm provides a better result in terms of Accuracy, F-measure, and SWCD.
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References
Shabanzadeh P, Yusof R (2015) An efficient optimization method for solving unsupervised data classification problems. Comput Math Methods Med 2015:802754. https://doi.org/10.1155/2015/802754
Kao Y-T, Zahara E, Kao I-W (2008) A hybridized approach to data clustering. Expert Syst Appl 34:1754–1762. https://doi.org/10.1016/j.eswa.2007.01.028
Sloss AN, Gustafson S (2020) 2019 Evolutionary algorithms review. In: Banzhaf W, Goodman E, Sheneman L et al (eds) Genetic programming theory and practice XVII. Springer International Publishing, Cham, pp 307–344
Zhang Y, Agarwal P, Bhatnagar V et al (2013) Swarm intelligence and its applications. Sci World J 2013:528069. https://doi.org/10.1155/2013/528069
Sharma M, Chhabra JK (2019) An efficient hybrid PSO polygamous crossover based clustering algorithm. Evol Intell. https://doi.org/10.1007/s12065-019-00235-4
University of california irvine, ucirvine machine learning repository, http://archive.ics.uci.edu/ml/index.php
Alswaitti M, Albughdadi M, Isa NAM (2019) Variance-based differential evolution algorithm with an optional crossover for data clustering. Appl Soft Comput 80:1–17. https://doi.org/10.1016/j.asoc.2019.03.013
Mustafa HMJ, Ayob M, Nazri MZA, Kendall G (2019) An improved adaptive memetic differential evolution optimization algorithms for data clustering problems. PLoS ONE 14:1–28. https://doi.org/10.1371/journal.pone.0216906
Gao H, Li Y, Kabalyants P et al (2020) A novel hybrid PSO-K-means clustering algorithm using Gaussian estimation of distribution method and Lévy flight. IEEE Access 8:122848–122863. https://doi.org/10.1109/ACCESS.2020.3007498
Nasiri J, Khiyabani FM (2018) A whale optimization algorithm (WOA) approach for clustering. Cogent Math Stat 5:1483565. https://doi.org/10.1080/25742558.2018.1483565
Das P, Das DK, Dey S (2018) A modified bee colony optimization (MBCO) and its hybridization with k-means for an application to data clustering. Appl Soft Comput 70:590–603. https://doi.org/10.1016/j.asoc.2018.05.045
Du Z, Han D, Li K-C (2019) Improving the performance of feature selection and data clustering with novel global search and elite-guided artificial bee colony algorithm. J Supercomput 75:5189–5226. https://doi.org/10.1007/s11227-019-02786-w
Das S, Abraham A, Konar A (2008) Automatic clustering using an improved differential evolution algorithm. IEEE Trans Syst Man, Cybern - Part A Syst Humans 38:218–237
Bouyer A, Hatamlou A (2018) An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms. Appl Soft Comput 67:172–182. https://doi.org/10.1016/j.asoc.2018.03.011
Yang L, Zhang W, Lai Z, Cheng Z (2018) A particle swarm clustering algorithm based on tree structure and neighbourhood. In: Li K, Li W, Chen Z, Liu Y (eds) Computational intelligence and intelligent systems. Springer, Singapore, pp 67–85
Kushwaha N, Pant M (2019) A teaching–learning-based particle swarm optimization for data clustering. In: Tanveer M, Pachori RB (eds) Machine intelligence and signal analysis. Springer, Singapore, pp 223–233
Kumar Y, Singh PK (2018) Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering. Appl Intell 48:2681–2697. https://doi.org/10.1007/s10489-017-1096-8
Prakash J, Singh PK (2018) Hybrid Gbest-guided artificial bee colony for hard partitional clustering. Int J Syst Assur Eng Manag 9:911–928. https://doi.org/10.1007/s13198-017-0684-7
Aljarah I, Mafarja M, Heidari AA et al (2020) Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach. Knowl Inf Syst 62:507–539. https://doi.org/10.1007/s10115-019-01358-x
Dhal KG, Das A, Ray S, Das S (2019) A clustering based classification approach based on modified cuckoo search algorithm. Pattern Recognit Image Anal 29:344–359. https://doi.org/10.1134/S1054661819030052
Tang Y, Wang N, Lin J, Liu X (2019) Using improved glowworm swarm optimization algorithm for clustering analysis. In: 2019 18th International symposium on distributed computing and applications for business engineering and science (DCABES). pp 190–194. https://doi.org/10.1109/DCABES48411.2019.00054
Li Y, Cai J, Yang H et al (2019) A novel algorithm for initial cluster center selection. IEEE Access 7:74683–74693. https://doi.org/10.1109/ACCESS.2019.2921320
Zabihi F, Nasiri B (2018) A novel history-driven artificial bee colony algorithm for data clustering. Appl Soft Comput 71:226–241. https://doi.org/10.1016/j.asoc.2018.06.013
Tripathi AK, Sharma K, Bala M (2018) A novel clustering method using enhanced grey wolf optimizer and MapReduce. Big Data Res 14:93–100. https://doi.org/10.1016/j.bdr.2018.05.002
Kumar V, Chhabra JK, Kumar D (2017) Grey wolf algorithm-based clustering technique. J Intell Syst 26:153–168. https://doi.org/10.1515/jisys-2014-0137
Hassanzadeh T, Meybodi MR (2012) A new hybrid approach for data clustering using firefly algorithm and K-means. In: The 16th CSI international symposium on artificial intelligence and signal processing (AISP 2012). pp 7–11. https://doi.org/10.1109/AISP.2012.6313708
Jadhav AN, Gomathi N (2018) WGC: Hybridization of exponential grey wolf optimizer with whale optimization for data clustering. Alex Eng J 57:1569–1584. https://doi.org/10.1016/j.aej.2017.04.013
Ghany KKA, AbdelAziz AM, Soliman THA, Sewisy AAE-M (2020) A hybrid modified step whale optimization algorithm with tabu search for data clustering. J King Saud Univ - Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2020.01.015
Li Y, Ni Z, Jin F et al (2018) Research on clustering method of improved glowworm algorithm based on good-point set. Math Probl Eng 2018:8724084. https://doi.org/10.1155/2018/8724084
Isimeto R, Yinka-Banjo C, Uwadia CO, Alienyi DC (2017) An enhanced clustering analysis based on glowworm swarm optimization. In: 2017 IEEE 4th International conference on soft computing & machine intelligence (ISCMI). pp 42–49. https://doi.org/10.1109/ISCMI.2017.8279595
Neshat M, Yazdi SF, Yazdani D, Sargolzaei M (2012) A new cooperative algorithm based on PSO and K-means for data clustering. J Comput Sci 8:188–194. https://doi.org/10.3844/jcssp.2012.188.194
Saida IB, Nadjet K, Omar B (2014) A New algorithm for data clustering based on cuckoo search optimization. In: Pan J-S, Krömer P, Snášel V (eds) Genetic and evolutionary computing. Springer International Publishing, Cham, pp 55–64
Bansal JC, Sharma H, Jadon SS, Clerc M (2014) Spider monkey optimization algorithm for numerical optimization. Memetic Comput 6:31–47. https://doi.org/10.1007/s12293-013-0128-0
Tang L, Liu J (1999) A comparison of tabu search and local search methods for single machine scheduling with ready tim. IFAC Proc 32:6127–6132
Misagh Rahbari AJ A hybrid simulated annealing algorithm for travelling salesman problem with three neighbor generation structures. In: 10th International conference of iranian operations research society (ICIORS 2017), University of Mazandaran, Babolsar, Iran. https://hal.archives-ouvertes.fr/hal-01962049
Sharma A, Sharma A, Panigrahi BK et al (2016) Ageist spider monkey optimization algorithm. Swarm Evol Comput 28:58–77. https://doi.org/10.1016/j.swevo.2016.01.002
Arasomwan M, Adewumi A (2013) On adaptive chaotic inertia weights in Particle Swarm Optimization. In: IEEE Symposium on swarm intelligence (SIS). pp 72–79. https://doi.org/10.1109/SIS.2013.6615161
Sharma N, Kaur A, Sharma H et al (2019) Chaotic spider monkey optimization algorithm with enhanced learning. In: Bansal JC, Das KN, Nagar A et al (eds) Soft computing for problem solving. Springer, Singapore, pp 149–161
Kwedlo W (2011) A clustering method combining differential evolution with the K-means algorithm. Pattern Recogn Lett 32:1613–1621. https://doi.org/10.1016/j.patrec.2011.05.010
Cura T (2012) A particle swarm optimization approach to clustering. Expert Syst Appl 39:1582–1588
Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recognit 33:1455–1465. https://doi.org/10.1016/S0031-3203(99)00137-5
Figueiredo D (2013) When is statistical significance not significant? Braz Polit Sci Rev. https://doi.org/10.1590/S1981-38212013000100002
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Patel, V.P., Rawat, M.K. & Patel, A.S. Local neighbour spider monkey optimization algorithm for data clustering. Evol. Intel. 16, 133–151 (2023). https://doi.org/10.1007/s12065-021-00647-1
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DOI: https://doi.org/10.1007/s12065-021-00647-1