Q1. Explain why Clustering is called “Unsupervised Learning” while Classificatio

Question

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? Marks

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? Marks

Q3. What is the difference between Single level Partition based clustering method vs. Hierarchical Clustering in terms of basic concept, strength and weakness?

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

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.

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.

Q7(a)Define following term Marks

i. Geodesic Distance

ii. Eccentricity

iii. Radius

iv. Diameter

v. peripheral vertex

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

i. Eccentricity

ii. Radius

iii. Diameter

iv. peripheral vertex

Q8. What are the challenges in Graph Clustering?

Explanation / Answer

I am answering intial 2 questions which contains 2 subparts. Its a long question, For remaining answers, you can post the question again.

Question1)
In clustering there is no predefined classes.
clustering is a task of inferring a function to describe hidden structure from unlabeled data that is why it is unsupervised learning.
while in classification we know the target variable to classify our data set hence it is supervised learning.

three applications of Cluster Analysis:

1)City-planning: According to house value,type and geographical location clustering can identifying groups of houses.
2)Marketing: clustering helps marketers to find out the different groups for the customers and use that information to create targeted marketing programs.
3)Insurance: Identifying groups of motor insurance policy holders with a high average claim cost.

Question2)
Ans: a)

K-Means Strength :

1) If the variables are more/huge, then K-Means clustering most of the times performs computationally faster than hierarchical clustering, if we keep k smalls.
2) Especially if the clusters are globular, K-Means clustering produce tighter clusters.

K-Means Weakness :

1) It is difficult to predict the k-Value.
2) Different initial partitions can result in different final clusters.
3) It would not work well with global cluster.
4) It does not work well with clusters of different density and different size.

Ans: b)
clustering methods that can be used with Numerical, categorical and mix data:
1) K-Means clustering.
2) Hierarchical culstering.

There are many more, but these two are quite popular.

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