Abstract
The task of data clustering is to distribute data objects into groups based on similarity. In this paper, a simplex method is applied to improve bacterial colony optimization (BCO) searching capacity (SMBCO). To solve the data clustering problem, the proposed SMBCO algorithm is used. The superiority proposed SMBCO method is assessed using five machine learning datasets. The clustering algorithm's results are assessed with objective value and computational time. The results of experiments indicated that the SMBCO model attains high precision when compared with a conventional algorithm with faster convergence.
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Babu, S.S., Jayasudha, K. (2022). A Simplex Method-Based Bacterial Colony Optimization for Data Clustering. In: Raj, J.S., Kamel, K., Lafata, P. (eds) Innovative Data Communication Technologies and Application. Lecture Notes on Data Engineering and Communications Technologies, vol 96. Springer, Singapore. https://doi.org/10.1007/978-981-16-7167-8_72
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DOI: https://doi.org/10.1007/978-981-16-7167-8_72
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