Topic List

Supervised Learning
Classification
Regression
Time Series Forecasting
Unsupervised Learning
Clustering
K-Means Clustering
Hierarchical Clustering
Semi-Supervised Learning
Reinforcement Learning(ML)
Deep Learning(ML)
Transfer Learning(ML)
Ensemble Learning
Explainable AI (XAI)
Bayesian Learning
Decision Trees
Support Vector Machines (SVMs)
Instance-Based Learning
Rule-Based Learning
Neural Networks
Evolutionary Algorithms
Meta-Learning
Multi-Task Learning
Metric Learning
Few-Shot Learning
Adversarial Learning
Data Pre Processing
Natural Language Processing(ML)

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Clustering QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

TIME:20:00

CORRECT: 0

INCORRECT: 0

Time:20:00

Question: 1

The goal of clustering is to-

Divide the data points into groups

Classify the data point into different classes

Predict the output values of input data points

All of the above

Question: 2

Clustering is a-

Supervised learning

Unsupervised learning

Reinforcement learning

None

Question: 3

Which of the following clustering algorithms suffers from the problem of convergence at local optima?

K- Means clustering

Hierarchical clustering

Diverse clustering

All of the above

Question: 4

Which version of the clustering algorithm is most sensitive to outliers?

K-means clustering algorithm

K-modes clustering algorithm

K-medians clustering algorithm

None

Question: 5

Which of the following is a bad characteristic of a dataset for clustering analysis-

Data points with outliers

Data points with different densities

Data points with non-convex shapes

All of the above

Question: 6

For clustering, we do not require-

Labeled data

Unlabeled data

Numerical data

Categorical data

Question: 7

Which of the following is an application of clustering?

Biological network analysis

Market trend prediction

Topic modeling

All of the above

Question: 8

On which data type, we can not perform cluster analysis?

Time series data

Text data

Multimedia data

None

Question: 9

Netflix’s movie recommendation system uses-

Supervised learning

Unsupervised learning

Reinforcement learning

All of the above

Question: 10

The final output of Hierarchical clustering is-

The number of cluster centroids

The tree representing how close the data points are to each other

A map defining the similar data points into individual groups

All of the above

Question: 11

Which of the step is not required for K-means clustering?

a distance metric

initial number of clusters

initial guess as to cluster centroids

None

Question: 12

Which is the following is wrong?

k-means clustering is a vector quantization method

k-means clustering tries to group n observations into k clusters

k-nearest neighbor is same as k-means

None

Question: 13

Which of the following uses merging approach?

Hierarchical clustering

Partitional clustering

Density-based clustering

All of the above

Question: 14

Which of the following is a method of choosing the optimal number of clusters for k-means?

cross-validation

the silhouette method

the elbow method

All of the above

Question: 15

When does k-means clustering stop creating or optimizing clusters?

After finding no new reassignment of data points

After the algorithm reaches the defined number of iterations

Both A and B

None

Question: 16

Which of the following clustering algorithm follows a top to bottom approach?

K-means

Divisible

Agglomerative

None

Question: 17

Which algorithm does not require a dendrogram?

K-means

Divisible

Agglomerative

All of the above

Question: 18

Which of the following clustering algorithms suffers from the problem of convergence at local optima?

Takes each data point as an individual cluster

Goes on making clusters until it reaches to an optimal number of cluster

Follows a top to bottom approach

All of the above

Question: 19

For topic modeling what should we use?

Random forest

Support vector machine

K-means

K-nearest neighbors

Question: 20

What is a dendrogram?

A hierarchical structure

A diagram structure

A graph structure

None