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Hierarchical clustering is an unsupervised machine learning algorithm that groups unlabeled datasets into clusters, represented by a dendrogram. It can be either agglomerative (bottom-up) or divisive (top-down), with advantages such as not needing to pre-specify the number of clusters, but it can struggle with large clusters and is sensitive to noise. Additionally, clustering can be categorized into hard and soft clustering, with hard clustering assigning each data point to one cluster and soft clustering a

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palapadusachivalayam

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EN