Abstract
Extracting useful information from a large-scale dataset and transforming it into the required structure for further use is termed data mining. One of the most important techniques of data mining is data clustering that is responsible for grouping the data into meaningful groups (clusters). Environmental Adaptation Method (EAM) is an optimization algorithm that has already proved its efficacy in solving global optimization problems. In this paper, an approach based on a new version of EAM has been suggested for solving the data clustering problem. To validate the utility of the suggested approach, four recently developed metaheuristics have been implemented and compared for six standard benchmark datasets. Various comparative performance analysis based on experimental values have justified the competitiveness and effectiveness of the suggested approach.
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References
Alpaydin, E.: Introduction to Machine Learning. MIT press, Cambridge (2014)
Halberstadt, W., Douglas, T.S.: Fuzzy clustering to detect tuberculous meningitis-associated hyperdensity in CT images. Comput. Biol. Med. 38(2), 165–170 (2008)
Tan, P.N.: Introduction to Data Mining. Pearson Education India, Chennai (2018)
Chen, C.Y., Ye, F.: Particle swarm optimization algorithm and its application to clustering analysis. In: 2012 Proceedings of 17th Conference on Electrical Power Distribution, pp. 789–794. IEEE (2012)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)
Kushwaha, N., Pant, M., Kant, S., Jain, V.K.: Magnetic optimization algorithm for data clustering. Pattern Recogn. Lett. 115, 59–65 (2018)
Han, X.H., Quan, L., Xiong, X.Y., Almeter, M., Xiang, J., Lan, Y.: A novel data clustering algorithm based on modified gravitational search algorithm. Eng. Appl. Artif. Intell. 61, 1–7 (2017)
Jadhav, A.N., Gomathi, N.: WGC: hybridization of exponential grey wolf optimizer with whale optimization for data clustering. Alex. Eng. J. 57(3), 1569–1584 (2018)
Singh, T., Shukla, A., Mishra, K.K.: Improved environmental adaption method with real parameter encoding for solving optimization problems. In: Advances in Computer and Computational Sciences, pp. 13–20. Springer, Cham (2018)
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)
Mirjalili, S., Mirjalili, S.M., Hatamlou, A.: Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Appl. 27(2), 495–513 (2016)
Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163–191 (2017)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Arora, S., Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft. Comput. 23(3), 715–734 (2019)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)
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Singh, T., Mishra, K.K., Ranvijay (2021). Data Clustering Using Environmental Adaptation Method. In: Abraham, A., Shandilya, S., Garcia-Hernandez, L., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2019. Advances in Intelligent Systems and Computing, vol 1179. Springer, Cham. https://doi.org/10.1007/978-3-030-49336-3_16
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DOI: https://doi.org/10.1007/978-3-030-49336-3_16
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