Abstract
Twin support vector clustering (TWSVC) algorithm has attracted a growing focus in recent years. In this paper, an enhanced TWSVC algorithm is proposed by using particle swarm optimization (PSO) for obtaining k-cluster planes and assigning each data sample to a correct cluster. This enhanced algorithm basically uses TWSVC to refine the clusters formed by PSO. The hybrid PSO-TWSVC algorithm is evaluated on a collected cobalt dataset in the nanotechnology field and then tested on four public datasets. The experiments of hybrid PSO-TWSVC with the classical K-means and TWSVC algorithms have proved that the hybrid PSO-TWSVC clustering algorithm has obvious advantages on accuracy.
Atrab A. Abd El-Aziz, Heba Al Shater, A. Dakhlaoui and Aboul Ella Hassanien: Scientific Research Group in Egypt (SRGE) http://www.egyptscience.net.
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Abd El-Aziz, A.A., Shater, H.A., Dakhlaoui, A., Hassanien, A.E., Gupta, D. (2020). Optimized Twin Support Vector Clustering in Transmission Electron Microscope of Cobalt Nanoparticles. In: Khanna, A., Gupta, D., Bhattacharyya, S., Snasel, V., Platos, J., Hassanien, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1087. Springer, Singapore. https://doi.org/10.1007/978-981-15-1286-5_73
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