Hierarchical Clustering
Hierarchical clustering is a method of clustering data objects based on their similarity. It is a bottom-up approach where each data point is initially considered as a separate cluster. Then, the algorithm iteratively merges the two closest clusters until all the data points belong to a single cluster. The result is a dendrogram, which is a tree-like diagram that shows the hierarchical relationship between the clusters. Hierarchical clustering can be agglomerative or divisive. In agglomerative clustering, each data point starts as a separate cluster and is successively merged with the closest cluster until all the data points belong to a single cluster. In divisive clustering, all the data points start in a single cluster and are successively split into smaller clusters until each data point is in its own cluster. Hierarchical clustering is widely used in data science and machine learning for exploratory data analysis, pattern recognition, and image segmentation.
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