Titlebook: Evolutionary Data Clustering: Algorithms and Applications; Ibrahim Aljarah,Hossam Faris,Seyedali Mirjalili Book 2021 The Editor(s) (if app

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Aggressive

Evaluation Research and Fundamental Researchrdingly, data clustering has an increasing interest in various applications involving health, humanities, and industry. Assessing the goodness of clustering has been widely debated across the history of clustering analysis, which led to the emergence of abundant clustering evaluation measures. The a

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https://doi.org/10.1007/978-3-642-82539-2ation algorithms is the Grey Wolf Optimizer (GWO). In this chapter, we use GWO on seven medical data sets to optimize the initial clustering centroids represented by the individuals of each population at each iteration. The aim is to minimize the distances between instances of the same cluster to pr

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GROSS

https://doi.org/10.1007/978-3-531-93197-5sts. However, no standard method has been established yet to obtain optimal solutions for all standard problems. In this research, we propose a two-phase approach: An improved k-Means algorithm for the clustering phase and a hybrid meta-heuristic based on Adaptive Particle Swarm—PSO and Grey Wolf Op

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Studienbücher zur Sozialwissenschaftblems. HHO algorithm processes a population of search space with two operations: Soft besiege and Hard besiege. One of main problems in the use of population-based algorithms is premature convergence. A premature stagnation of the search creates a shortage of diversity, which affects the relationshi

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https://doi.org/10.1057/9780230615489milarity/dissimilarity between these instances. This process is called unsupervised learning. Detecting the data structure is not an easy task, especially if there are no previous assumptions to guide the clustering process. Multi-objective evolutionary algorithms have been used as alternative solut

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