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FRM Part I · FRM Exam Part I · Machine Learning and Prediction

Which statement correctly contrasts agglomerative hierarchical clustering with K-means?

Agglomerative hierarchical clustering begins with every observation as its own cluster and repeatedly merges the closest clusters, recording the merges in a dendrogram. Unlike K-means, it does not require the number of clusters in advance, and splitting from one cluster is the divisive approach.

  1. AHierarchical clustering requires the number of clusters to be fixed before it is run
  2. BAgglomerative clustering starts with each observation as its own cluster and successively merges the closest ones, producing a dendrogramCorrect
  3. CK-means produces a dendrogram that can be cut at any level
  4. DAgglomerative clustering starts with one cluster and repeatedly splits it

Explanation

Agglomerative methods are bottom-up: each point begins alone and the closest clusters are merged, with the history shown in a dendrogram. K-means needs K specified in advance and gives no dendrogram. Starting from one cluster and splitting describes divisive clustering.

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