Q1. Explain why Clustering is called “Unsupervised Learning” while Classification iscalled “Supervised Learning”? Give three applications of Cluster Analysis and giveexamples on each? Marks [0.25+0.25]Q2. (a) What are the strength and weakness of the k-Means Clustering Partitioningmethod?(b)What are the clustering methods that can be used with Numerical, categorical and mixdata? Marks [0.25+0.25]Q3. What is the difference between Single level Partition based clustering method vs.Hierarchical Clustering in terms of basic concept, strength and weakness?Marks [0.5]Q4(a). What do we aim for to have a good quality clustering in terms of Cohesiveness,and Distinctiveness?(b) List and briefly describe the three Clustering Measure of Quality? Marks [0.25+0.25]Q5. Many partitional clustering algorithms that automatically determine the number ofclusters claim that this is an advantage. List two situations in which this is not the case.Marks [0.5]Q6. Suppose we find K clusters using Ward’s method, bisecting K-means, and ordinaryK-means. Which of these solutions represents a local or global minimum? Explain. Marks [0.5]Q7(a)Define following term Marks [0.25+0.50]i. Geodesic Distanceii. Eccentricityiii. Radiusiv. Diameterv. peripheral vertex(b). Measurements based on geodesic distance consider graph G in given figure andcalculate following termi. Eccentricityii. Radiusiii. Diameteriv. peripheral vertexQ8. What are the challenges in Graph Clustering? Marks [0.25]