Abstract
Heuristic algorithms are widely used in the diverse fields of engineering and sciences and prove its efficiency over classical algorithms. In the analysis of chemical process, it is observed that the formation of new product consists of a proficient computational procedure among chemical reactions. These chemical reactions consist of objects, events, states, and process. In this work, an efficient and robust algorithm, called artificial chemical reaction optimization algorithm, is adopted for solving the partitional clustering problems. The performance of the proposed algorithm is investigated on well-known clustering datasets. Further, the simulation results of the CRO-based clustering algorithm are compared with some state-of-the-art clustering algorithms. It is seen that proposed clustering algorithm provides better performance than other algorithms in terms of intra-cluster distance and f-measure.
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Kumar, Y., Dahiya, N., Malik, S., Yadav, G., Singh, V. (2019). Chemical Reaction-Based Optimization Algorithm for Solving Clustering Problems. In: Li, X., Wong, KC. (eds) Natural Computing for Unsupervised Learning. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-98566-4_7
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DOI: https://doi.org/10.1007/978-3-319-98566-4_7
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