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Automatic Data Clustering Using Farmland Fertility Metaheuristic Algorithm

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Advances in Swarm Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1054))

Abstract

Data clustering is a data mining task, and it means finding clusters from among data whose labels are not predetermined. It is a popular analytics tool for statistical data in various domains. The k-means algorithm is a basic algorithm for data clustering, which has initial problems such as dependence on the cluster centers’ initial value, sensitivity to outliers, and non-guaranteed optimal solutions to unbalanced cluster formation. This book chapter uses Farmland Fertility Algorithm (FFA) for the data clustering algorithm. Ten standard data sets used to evaluate the effectiveness of FFA are compared Harmony Search (HS), Monarch Butterfly Optimization (MBO), Artificial Bee Colony (ABC), Symbiotic Organism Search (SOS), Differential Evolution (DE), and Crow Search Algorithm (CSA) in terms of statistical criteria such as analysis of variance (ANOVA) and the convergence rate. Experimental results demonstrate that FFA has better performance than other optimization algorithms and is more stable than these algorithms.

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Correspondence to Farhad Soleimanian Gharehchopogh .

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Gharehchopogh, F.S., Shayanfar, H. (2023). Automatic Data Clustering Using Farmland Fertility Metaheuristic Algorithm. In: Biswas, A., Kalayci, C.B., Mirjalili, S. (eds) Advances in Swarm Intelligence. Studies in Computational Intelligence, vol 1054. Springer, Cham. https://doi.org/10.1007/978-3-031-09835-2_11

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