1. Which statement is NOT TRUE about k-means clustering?
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.
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?
4. Which approach can be used to calculate dissimilarity of objects in clustering?
All of the above(Minkowski distance, Euclidian distance, Cosine similarity)
5. How is a center point (centroid) picked for each cluster in k-means?
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.