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Agglomerative Clustering Explained by Shameem Xs is a document available to read on EtoBox.
What is Agglomerative Clustering Explained about?
The document describes agglomerative hierarchical clustering using a example dataset with 6 data points (a, b, c, d, e, f). It explains that initially each data point starts in its own cluster. The two closest clusters are then merged, and the distances between all clusters are recalculated. This process repeats, each time merging the closest two clusters and updating distances, until all data points are in a single cluster. The document outlines different methods for calculating distances between clusters
- Author
- Shameem Xs
- Language
- EN