Which of the following distance metrics is commonly used in hierarchical clustering?Cosine similarityEuclidean distanceJaccard indexHamming distance
Question
Which of the following distance metrics is commonly used in hierarchical clustering?Cosine similarityEuclidean distanceJaccard indexHamming distance
Solution
Hierarchical clustering commonly uses the Euclidean distance metric. This metric is based on the "straight line" distance between two points in a space, making it suitable for clustering in many contexts. However, it's important to note that the choice of distance metric in hierarchical clustering can depend on the specific characteristics of the data and the goals of the analysis. Other metrics like Cosine similarity, Jaccard index, and Hamming distance can also be used in certain situations.
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
Which distance metric is commonly used in k-Nearest Neighbors (KNN) for numerical data?a.Cosine similarityb.Jaccard distancec.Euclidean distanced.Hamming distance
The metric is commonly used to evaluate the performance of clustering algorithms.
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?linkage(X, method='single', metric='euclidean')dendrogram(Z)metric='euclidean'X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
What is the most widely used distance metric in KNN?Euclidean distanceManhattan distancePerpendicular distanceAll of the aboveClear selection
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