Özet:

Abstract For decades, the researchers have developed many ways as optimization procedures with the aim of find the best solution in short time for a many problems under certain conditions in the field of engineering, medicine and banking. These ways have been also used for parameter updating of algorithms. The most popular Optimization algorithms methods known are mining classification and clustering. In this article, the clustering has been used to identify the most important point in the best cluster centers of set data. Artificial Algae Algorithm (AAA) optimization algorithm was used in the clustering process and implemented on UCI datasets. Balance, Breast Cancer Wisconsin Diagnostic, Breast Cancer Wisconsin original, Pima Diabetes, Glass, Iris, Wine, Urban Land Cover and Hill Valley UCI datasets are used to assess the performing of the Algae Algorithm-based clustering algorithm. Euclides method was used to calculate the distance between the data. The performance of the AAA based clustering algorithm, Total square distance values in different iteration numbers were calculated for each data set. The total square error rate value was calculated for each iteration and as the number of iterations progresses, the total square error rate value decreases smoothly.  The results obtained were compared with k-means, Differential Evolution (DE), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) clustering algorithms. According to the experimental results in this study, the proposed AAA-based clustering algorithm achieved better results in iris and wine data sets compared to other clustering algorithms, while it obtained close to good results in other data sets. As a result, the Artificial Algae Algorithm-based clustering algorithm showed that the method showed a fairly stable appearance and the performance of the clusters also increased, which shows that this study successfully achieved its purpose.

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