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K-means vs Hierarchical Clustering by Dhananjay Sharma is a document available to read on EtoBox.
Hierarchical clustering builds a hierarchy of clusters by successively merging or splitting clusters based on a distance metric. It can be agglomerative, starting with each observation as a separate cluster and merging the closest pairs, or divisive, starting with all observations in one cluster and splitting them recursively. K-means clustering assigns each observation to the cluster with the nearest centroid, and iteratively updates cluster centroids until convergence. Web content mining analyzes unstru
- Author
- Dhananjay Sharma
- Language
- EN