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A comparison between neural network clustering (NNC), hierarchical clustering (HC) and K-means clustering (KMC) is performed to evaluate the computational superiority of these three machine learning (ML) techniques for organizing large datasets into clusters. For NNC, a self-organizing map (SOM) training was applied to a collection of wavefront sen...
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... generated using the Machine Learning (ML) program "clusterdata." This tree represents hierarchical stacks of clusters across different levels, each level grouping variables that demonstrate a similar influence on the overall system. Applying the clusterdata function to the normalized values of a 15-variable dataset helped construct a dendrogram (Fig. 6) using a dissimilarity matrix and a linkage matrix. The dissimilarity matrix quantified the distance between each pair of variables, while the linkage matrix identified connections between variable pairs or clusters. Moreover, the linkage function calculated distances not only between individual variables but also between clusters or ...
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... Machine Learning (ML) algorithm, *clusterdata*, was utilized to generate a Dendrogram cluster tree, as depicted in Fig. 6. This tree illustrates different hierarchical levels, each containing clusters of Zernike variables. The integration of a dissimilarity function and a linkage function enabled the pairing of clusters or the association of a variable with a cluster based on their closest proximity distances. To determine whether the grouping of ...
