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Which distance metric is commonly used in K-means clustering?
Which distance metric is commonly used in K-means clustering?
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Practice Questions
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Which distance metric is commonly used in K-means clustering?
Manhattan distance
Cosine similarity
Euclidean distance
Hamming distance
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K-means typically uses Euclidean distance to measure the distance between data points and centroids.
Questions & Step-by-step Solutions
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Q: Which distance metric is commonly used in K-means clustering?
Solution:
K-means typically uses Euclidean distance to measure the distance between data points and centroids.
Steps: 5
Show Steps
Step 1: Understand that K-means clustering is a method used to group similar data points together.
Step 2: Know that in K-means, we need to measure how far apart the data points are from the center of their group, called a centroid.
Step 3: The distance between data points and centroids is measured using a formula.
Step 4: The most common formula used for this measurement is called Euclidean distance.
Step 5: Euclidean distance calculates the straight-line distance between two points in space.
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