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In the K-Means algorithm, we have to specify the number of clusters.

• 
  

• • True

• • False

What metric can be used to find an optimal number of clusters ?

• • R Squared

• • MSE

• • WCSS

We can choose any random initial centroids at the beginning of K-Means.

• • True

• • False

What is step 1 in K-Means?

• • Choose the number of Decision Trees

• • Choose the number of K of clusters

• • Remove all K clusters

• • None of the above

Which method can be used to determine the optimal number of clusters in K-Means?

• • Elbow Method

• • Decision Tree Depth

• ROC Curve

• Gradient Descent

In K-Means++, how are initial centroids chosen?

• • Randomly from a Gaussian distribution

• • By sorting the data first

• • Probabilistically, favoring points farther from existing centroids

• • From the first k points in the dataset

The goal of clustering a set of data is to

• divide them into groups of data that are near each other

• choose the best data from the set

• determine the nearest neighbors of each of the data

• predict the class of data

K-Means clustering stops iterating when:

• The learning rate becomes zero

• The number of epochs exceeds 100

• Centroids no longer change significantly between iterations

• All samples are correctly classified

What type of learning does K-Means clustering belong to?

• Supervised Learning

• Unsupervised Learning

• Semi-Supervised Learning

• Reinforcement Learning

Which distance measure is used by K-means clustering algorithm?

• Hamming Distance

• Euclidean Distance

• Manhattan Distance

• Minkowski Distance