CS 451 Quiz 19
Clustering and K-means
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While running the K-means algorithm, the cost function J might fluctuate up and down before converging *
The K-means algorithm might converge to a local minimum that is not the global minimum *
Which are steps of the K-means algorithm? *
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The cost function J in the K-means algorithm is also called *
In the last video on K-means, what example is used to illustrate the trade-off between different choices for K? *
The optimization objective J in the K-means algorithm measures *
In unsupervised learning, the training set consists of data points {x1, x2, x3, ...} and *
In K-means, what are reasonable ways of to choose the number of clusters K? *
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How should the cluster centers be initialized for K-means? *
Which of the following is not an application of clustering? *
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