Clustering MCQ's

1.  of a set of points is defined using a distance measure .

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   Similarity

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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   Agglo

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

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   True

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

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   False

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

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   Dendogram

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

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   False

7. measures the goodness of a cluster

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   Clusteroid

8. of two points is the average of the two points in Eucledian Space.

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   Centroid

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

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   True

10. K Means algorithm assumes Eucledian Space/Distance

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   True

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

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   Convergence

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

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   o(N2)

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

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   True

14. is the data point that is closest to the other point in the cluster.

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   Clusteroid

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

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   Kmeans()