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Questions and Answers
What is the main goal of clustering?
What is the main goal of clustering?
Which type of learning are clustering methods categorized under?
Which type of learning are clustering methods categorized under?
How are points within the same cluster expected to be?
How are points within the same cluster expected to be?
What is one common application of clustering in business?
What is one common application of clustering in business?
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Which clustering technique involves finding centroids and updating the cluster assignments iteratively?
Which clustering technique involves finding centroids and updating the cluster assignments iteratively?
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In clustering, what does it mean to say points in the same group are 'quite similar'?
In clustering, what does it mean to say points in the same group are 'quite similar'?
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What is the key property of the clusters in K-Means Clustering?
What is the key property of the clusters in K-Means Clustering?
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What is the objective of K-Means Clustering?
What is the objective of K-Means Clustering?
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How is the within-cluster variation W(Ck) defined in K-Means Clustering?
How is the within-cluster variation W(Ck) defined in K-Means Clustering?
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What is the optimization problem that defines K-Means Clustering?
What is the optimization problem that defines K-Means Clustering?
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What is one of the weaknesses of K-Means Clustering mentioned in the text?
What is one of the weaknesses of K-Means Clustering mentioned in the text?
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What is one of the strengths of K-Means Clustering mentioned in the text?
What is one of the strengths of K-Means Clustering mentioned in the text?
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What is the main objective of K-means clustering?
What is the main objective of K-means clustering?
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In K-means clustering, what is done at the end of the 1st iteration?
In K-means clustering, what is done at the end of the 1st iteration?
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What criterion is used to determine when to stop the K-means clustering process?
What criterion is used to determine when to stop the K-means clustering process?
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Which property must sets 𝐶1, ..., 𝐶𝑘 satisfy in K-means clustering?
Which property must sets 𝐶1, ..., 𝐶𝑘 satisfy in K-means clustering?
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What is the purpose of finding the Euclidean distance between cluster center and each point in K-means clustering?
What is the purpose of finding the Euclidean distance between cluster center and each point in K-means clustering?
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Which step in K-means clustering involves recalculating the cluster centers based on the points assigned to each cluster?
Which step in K-means clustering involves recalculating the cluster centers based on the points assigned to each cluster?
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