1.Which statement is NOT TRUE about k-means clustering?
3 points
The objective of k-means, is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters.
As k-means is an iterative algorithm, it guarantees that it will always converge to the global optimum.
2.Which of the following are characteristics of DBSCAN? Select all that apply.
3 points
DBSCAN can find arbitrarily shaped clusters.
DBSCAN can find a cluster completely surrounded by a different cluster.
DBSCAN has a notion of noise, and is robust to outliers.
DBSCAN does not require one to specify the number of clusters such as k in k-means
3.Which of the following is an application of clustering?
3 points
Customer churn prediction
Price estimation
Customer segmentation
Sales prediction
4.Which approach can be used to calculate dissimilarity of objects in clustering?
3 points
Minkowski distance
Euclidian distance
Cosine similarity
All of the above
5.How is a center point (centroid) picked for each cluster in k-means?
3 points
We can randomly choose some observations out of the data set and use these observations as the initial means.
We can create some random points as centroids of the clusters.
We can select it through correlation analysis.
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