CS 451 Quiz 19
Clustering and Kmeans
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While running the Kmeans algorithm, the cost function J might fluctuate up and down before converging
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True
False
The Kmeans algorithm might converge to a local minimum that is not the global minimum
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True
False
Which are steps of the Kmeans algorithm?
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Check all that apply
cluster assignment
updating centroids
updating K
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The cost function J in the Kmeans algorithm is also called
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the distortion function
the distance function
the kernel function
In the last video on Kmeans, what example is used to illustrate the tradeoff between different choices for K?
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selecting shoe sizes
selecting Tshirt sizes
grouping test scores
grouping dinner choices
The optimization objective J in the Kmeans algorithm measures
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the sum of the distances from each data point to the nearest cluster center
the sum of the squared distances from each data point to the nearest cluster center
the sum of the maximum distances to the cluster center within each cluster
the maximum of the sum of the distances to the cluster center within each cluster
In unsupervised learning, the training set consists of data points {x1, x2, x3, ...} and
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a separate set of labels {y1, y2, y3, ...} without knowing which data point corresponds to which label
no labels at all
In Kmeans, what are reasonable ways of to choose the number of clusters K?
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Check all that apply
Manually, using prior knowledge of the problem to be solved
Using the "elbow" method
Using the "camel's hump" method
Let the Kmeans algorithm converge to the correct K
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How should the cluster centers be initialized for Kmeans?
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pick K random data points
pick K random points within the bounding box containing the data points
equally distribute K points within the bounding box containing the data points
Which of the following is not an application of clustering?
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Organizing computer clusters
Distinguishing good chess moves from bad chess moves
Market segmentation
Astronomical data analysis
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