Machine Learning 1: UNIT 5 (B) : Clustering Questions

                                                                         UNIT 5 (B)

                                                                     Clustering

5.     How does clustering work

6.     finding similarities using distances

7.     Euclidean distance and other distance metrics

k-Means Clustering:

8.     Plotting customers with their segments

9.     normalizing features

10.  cluster centres and interpreting the Clusters

11.  Hierarchical Clustering

 

UNIT-5 (B)

Long Answer Questions

1.    5.     Explain Hierarchical Clustering algorithm with example.

6.     What is the purpose of normalizing features?  How it can perform? Explain.

7.     How can you find the similarities using distances? List the different distance metrics.

8.     How can you be plotting the customers with their segments? Explain.

 

Short Answer Questions

1. 

4.     What is the purpose of cluster centre?

5.     How can you define cluster?

6.     What is the clustering algorithm?

7.     How can you identify the similarities?

8.     Euclidian Distance.

9.     Define Normalization features.

10.  List the techniques can be used for discovering the possible number of clusters?

11.  Explain Dendrogram.

12.  Explain Elbow Method

13.  Define Hierarchical Clustering Algorithm.