Which of the following distance metrics is commonly used in hierarchical clustering?Euclidean distanceCosine similarityHamming distanceJaccard index
Question
Which of the following distance metrics is commonly used in hierarchical clustering?Euclidean distanceCosine similarityHamming distanceJaccard index
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 continuous data.
However, it's important to note that the choice of distance metric in hierarchical clustering is not limited to Euclidean distance. Other metrics like Cosine similarity, Hamming distance, and Jaccard index can also be used depending on the nature of the data and the specific requirements of the analysis.
For instance, Cosine similarity is often used when the data is high-dimensional (like text data), Hamming distance is used for categorical data, and Jaccard index is used when the data is binary.
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