Description: Merging Clusters is a technique used in clustering where two or more clusters are combined based on certain criteria to improve the overall structure of the clustering. This technique falls under unsupervised learning, which seeks to identify patterns and structures in data without predefined labels. Merging Clusters allows for the optimization of data representation, facilitating the identification of more coherent and meaningful groups. By combining clusters, redundancies can be reduced, and the interpretation of results can be enhanced, which is especially useful in large and complex datasets. The fusion can be based on different metrics, such as the distance between cluster centroids or the similarity of the elements they contain. This technique is relevant in various fields, such as market segmentation, computational biology, and social network analysis, where identifying significant groups can provide valuable insights for decision-making. In summary, Merging Clusters is a powerful tool in unsupervised learning that improves the quality and utility of clustering results.
