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Clojure for Machine Learning by Akhil Wali

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Using hierarchical clustering

Hierarchical clustering is another method of cluster analysis in which input values from the training data are grouped together into a hierarchy. The process of creating the hierarchy can be done in a top-down approach, where in all observations are first part of a single cluster and are then divided into smaller clusters. Alternatively, we can group the input values using a bottom-up methodology, where each cluster is initially a sample value from the training data and these clusters are then combined together. The former top-down approach is termed as divisive clustering and the later bottom-up method is called agglomerative clustering.

Thus, in agglomerative clustering, we combine clusters into larger clusters, ...

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