Clustering with unsupervised learning and key concepts of it

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* 1. Please choose which method is NOT designed for discovering number of clusters (k) for K Means algorithms:

Elbow method.

Average silhouette method.

None of the variants.

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* 2. What are the disadvantages of DBSCAN:

Does not work well when dealing with clusters of varying densities.

One should provide number of clusters for the algorithm.

Does not work well with high dimensional data.

Does not include outliers.

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* 3. What features are highly correlated with target in wine dataset:

malic_acid.

ash.

proline.

hue.

total_phenols.

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* 4. What function is used to feed data to K Means clustering?

fit().

cluster().

load_data().

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* 5. Does DBSCAN expand it’s clusters while iterating over points?

True.

False.

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* 6. In order to choose number of clusters (k) for K Means using Elbow method we should recompute K Means every time for a new number of clusters (3, 4, 5, etc..).

True.

False.

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* 7. When using K Means results will be different each time, if we don’t set up random seed:

False.

True.

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* 8. What is result after executing this line of code kmeans.cluster_centers_.shape:

(3, 13).

(2, 18).

(3, 12).

(2, 12).