Recall & Review
beginner
What is the main goal of K-Means clustering?
The main goal of K-Means clustering is to group data points into K clusters where each point belongs to the cluster with the nearest mean, minimizing the distance within clusters.
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beginner
How does K-Means algorithm update cluster centers during training?
K-Means updates cluster centers by calculating the mean (average) of all data points assigned to each cluster, then moving the center to this mean position.
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beginner
What is the role of the 'K' in K-Means clustering?
'K' represents the number of clusters you want to divide your data into before running the algorithm.
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intermediate
Why might K-Means clustering give different results on different runs?
Because K-Means starts with random initial cluster centers, different starting points can lead to different final clusters.
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beginner
What metric does K-Means clustering typically use to assign points to clusters?
K-Means typically uses Euclidean distance to measure how close a data point is to each cluster center.
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What does K-Means clustering require you to decide before running the algorithm?
How does K-Means assign a data point to a cluster?
What happens to cluster centers after assigning points in K-Means?
Which of these is a common problem with K-Means clustering?
What type of learning is K-Means clustering?
Explain how the K-Means clustering algorithm works step-by-step.
Describe why choosing the right number of clusters (K) is important in K-Means clustering.