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What is the primary objective of the K-means clustering algorithm?
What is the primary objective of the K-means clustering algorithm?
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Practice Questions
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What is the primary objective of the K-means clustering algorithm?
To minimize the distance between points in the same cluster
To maximize the distance between different clusters
To create a hierarchical structure of clusters
To classify data into predefined categories
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K-means aims to minimize the distance between points within the same cluster by assigning points to the nearest centroid.
Questions & Step-by-step Solutions
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Q: What is the primary objective of the K-means clustering algorithm?
Solution:
K-means aims to minimize the distance between points within the same cluster by assigning points to the nearest centroid.
Steps: 6
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Step 1: Understand that K-means is a method used to group similar data points together.
Step 2: Know that each group is called a 'cluster'.
Step 3: Realize that K-means uses 'centroids', which are the center points of each cluster.
Step 4: Learn that the goal of K-means is to make sure that data points in the same cluster are as close to each other as possible.
Step 5: Understand that K-means does this by assigning each data point to the nearest centroid.
Step 6: Remember that the overall aim is to minimize the distance between points in the same cluster.
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