Given the following code snippet for performing hierarchical clustering in Python: from scipy.cluster.hierarchy import linkage, dendrogramimport matplotlib.pyplot as plt X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]Z = linkage(X, method='single', metric='euclidean')dendrogram(Z)plt.show() Which part of the code specifies the method for determining the distance between clusters?X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]linkage(X, method='single', metric='euclidean')metric='euclidean'dendrogram(Z)BackSkipNextFinish
Question
Given the following code snippet for performing hierarchical clustering in Python: from scipy.cluster.hierarchy import linkage, dendrogramimport matplotlib.pyplot as plt X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]Z = linkage(X, method='single', metric='euclidean')dendrogram(Z)plt.show() Which part of the code specifies the method for determining the distance between clusters?X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]linkage(X, method='single', metric='euclidean')metric='euclidean'dendrogram(Z)BackSkipNextFinish
Solution
The part of the code that specifies the method for determining the distance between clusters is linkage(X, method='single', metric='euclidean'). Specifically, the method='single' part indicates that the single linkage method is used for clustering, and metric='euclidean' specifies that the Euclidean distance is used as the distance metric.
Similar Questions
In hierarchical clustering, the distance between clusters is typically measured using the ______________ criterion.Select one:a.Cosine similarityb.Manhattan distancec.Linkage criteriond.Euclidean distance
How is the optimal number of clusters determined in hierarchical clustering?*1 pointBy minimizing the between-cluster sum of squaresBy maximizing the within-cluster sum of squaresBy examining the dendrogram and selecting an appropriate cut-off pointBy using the elbow method on the resulting tree structure
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