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Q1. Explain why Clustering is called “Unsupervised Learning” while Classification is called “Supervised Learning”? Give three applications of Cluster Analysis and give examples on each?

Q2.  (a)  What are the strength and weakness of the k-Means Clustering Partitioning method?

(b)What are the clustering methods that can be used with Numerical, categorical and mix data? 

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 of clusters 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 ordinary K-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]

Geodesic Distance

Eccentricity

Radius

Diameter

peripheral vertex

(b). Measurements based on geodesic distance consider graph G in given figure and calculate following term

Eccentricity

Radius

Diameter

peripheral vertex

Q8. What are the challenges in Graph Clustering?                                         Marks [0.25]