Clustering - The Data Ensemble MCQ's

1.  What is a preferred distance measure while dealing with sets ?

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   Jaccard 

2. Each point is a cluster in itself. We then combine the two nearest clusters into one. What type of clustering does this represent ?

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   Agglomerative 

3. Which learning is the method of finding structure in the data without labels.

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   Unsupervised

4. ____________ of a set of points is defined using a distance measure .

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   Similarity 

5. Members of the same cluster are far away / distant from each other .

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   False

6. Unsupervised learning focuses on understanding the data and its underlying pattern.

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   True

7. ___________ of two points is the average of the two points in Eucledian Space.

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   Centroid  

8. A centroid is a valid point in a non-Eucledian space .

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   False

9. What is the overall complexity of the the Agglomerative Hierarchical Clustering ?

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   O(N^3) 

10. ___________ measures the goodness of a cluster.

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   Cohesion 

11. ___________ is the data point that is closest to the other point in the cluster.

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   Clusteroid

12. The ______ is a visual representation of how the data points are merged to form clusters.

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   Dendogram 

13. _____________ is when points don't move between clusters and centroids stabilize.

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   Convergence 

14. ___________ is a way of finding the k value for k means clustering.

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   Cross Validation 

15. The number of rounds for convergence in k means clustering can be lage.

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   True 

16. Sampling is one technique to pick the initial k points in K Means Clustering.

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   True

17. K Means algorithm assumes Eucledian Space/Distance.

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   True

18. What is the R Function to divide a dataset into k clusters ?

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   kclusters() 

19. What is the R function to apply hierarchical clustering to a matrix of distance objects ?

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   hclust()