1. What is Mean Shift clustering primarily used for in data mining?
a) Classification
b) Regression
c) Clustering
d) Dimensionality reduction
Answer: c) Clustering
2. How does Mean Shift clustering determine the number of clusters?
a) By specifying the number of clusters beforehand
b) By using a hierarchical approach
c) By finding modes or peaks in the density of data points
d) By random assignment of data points
Answer: c) By finding modes or peaks in the density of data points
3. What does the “kernel function” in Mean Shift clustering determine?
a) The number of clusters
b) The shape and size of the clusters
c) The distance metric used for clustering
d) The computational complexity of the algorithm
Answer: b) The shape and size of the clusters
4. Which of the following statements about Mean Shift clustering is true?
a) It requires the number of clusters to be known in advance
b) It is sensitive to noise and outliers
c) It is computationally efficient for large datasets
d) It cannot handle non-linear relationships between data points
Answer: b) It is sensitive to noise and outliers
5. How does Mean Shift clustering handle data points during the clustering process?
a) By assigning each point to the nearest centroid
b) By forming clusters around the mean of the data points
c) By iteratively shifting each point towards the mode of its local density
d) By ignoring points that do not fit any cluster
Answer: c) By iteratively shifting each point towards the mode of its local density
6. What does the bandwidth parameter control in Mean Shift clustering?
a) The maximum distance between two points to be considered neighbors
b) The shape and size of the kernel function
c) The number of iterations for convergence
d) The number of clusters to form
Answer: b) The shape and size of the kernel function
7. Which of the following is an advantage of Mean Shift clustering?
a) It requires the number of clusters to be specified beforehand
b) It can handle clusters of varying shapes and sizes
c) It is sensitive to the initial placement of centroids
d) It is computationally expensive
Answer: b) It can handle clusters of varying shapes and sizes
8. What is the computational complexity of Mean Shift clustering?
a) O(n log n)
b) O(n^2)
c) O(n)
d) O(n*k)
Answer: b) O(n^2), where n is the number of data points.
9. How does Mean Shift clustering determine convergence?
a) When the mean of the data points remains constant
b) When the centroids stop moving significantly between iterations
c) When all data points are assigned to a cluster
d) After a fixed number of iterations
Answer: b) When the centroids stop moving significantly between iterations
10. What is a limitation of Mean Shift clustering?
a) It requires the number of clusters to be known in advance
b) It cannot handle high-dimensional data
c) It is sensitive to the choice of the kernel function
d) It does not handle noise and outliers well
Answer: c) It is sensitive to the choice of the kernel function
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