Artificial Algae Algorithm for Clustering of Benchmark Datasets

Abstract

The best solution found for a problem under specific circumstances is called optimization. Algorithms for optimization can make the best use of the information at their disposal. Numerous optimization algorithms have been created thus far by researchers, and most of these algorithms are based on the characteristics of naturally occurring biological organisms. Optimization algorithms have proven to be highly effective in numerous fields, including finance, engineering, and medical. Apart from these applications, they have also been employed in data mining techniques including clustering and classification. In many different domains, the clustering method is widely applied. Finding the optimum cluster centers is the most crucial step in the clustering process. In this study, the Artificial Algae Algorithm (AAA) is used to perform the clustering procedure by using 12 datasets that were taken from the UCI Machine Learning Repository. For every dataset, the squared distance values between the cluster centers and the data were computed in order to assess the effectiveness of AAA. The study evaluated AAA's performance against that of the ALO, DEA, MFO, PSO, TSA, and WOA algorithms. The clustering performance of AAA on benchmark datasets was measured to be better than the performance of other algorithms.

Keywords

• Artificial Algae Algorithm
• Benchmark Datasets
• Clustering
• Optimization

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