Types of Unsupervised Learning: Comprehensive Guide for Machine Learning

Question 1

Which of these is a supervised learning algorithm?

Accepted Answer

Linear regression

Answer

Clustering

Answer

Dimensionality reduction

Question 2

Which of the following is NOT a type of unsupervised learning algorithm?

Accepted Answer

Regression

Answer

Clustering

Answer

Dimensionality reduction

Answer

Anomaly detection

Question 3

Which of the following is NOT a type of unsupervised learning algorithm?

Accepted Answer

Supervised Learning

Answer

Dimensionality Reduction

Answer

Anomaly Detection

Answer

Clustering

Question 4

Which unsupervised learning algorithm is used to discover hidden patterns in datasets by identifying groups of similar data points?

Accepted Answer

Clustering

Answer

Dimensionality reduction

Answer

Reinforcement learning

Answer

Supervised learning

Question 5

Which unsupervised learning algorithm is used to reduce the dimensionality of a dataset while preserving its key features, making it easier to visualize and analyze?

Accepted Answer

Dimensionality reduction

Answer

Regression

Answer

Classification

Answer

Anomaly detection

Question 6

Which unsupervised learning algorithm is used to identify data points that significantly deviate from the rest of the data, potentially indicating anomalies or outliers?

Accepted Answer

Anomaly detection

Answer

Clustering

Answer

Classification

Answer

Dimensionality reduction

Question 7

Which clustering algorithm assigns data points to clusters based on their distance from a central point called the centroid?

Accepted Answer

K-Means clustering

Answer

Gaussian Mixture Models

Answer

Hierarchical clustering

Answer

Density-based clustering

Question 8

Which dimensionality reduction algorithm uses linear transformations to project data onto a lower-dimensional subspace, allowing for easier visualization and analysis?

Accepted Answer

Principal Component Analysis (PCA)

Answer

T-distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Answer

Uniform Manifold Approximation and Projection (UMAP)

Question 9

Which of the following is a key advantage of using unsupervised learning?

Accepted Answer

It can be applied to datasets without labeled data.

Answer

It requires a small amount of data to be effective.

Answer

It is easy to interpret and evaluate the results.

Answer

It is more accurate than supervised learning for all tasks.

Question 10

Which of the following is a potential limitation of using unsupervised learning?

Accepted Answer

It can be challenging to evaluate the quality of the results since there is no ground truth.

Answer

It requires a large amount of data to be effective.

Answer

It is less accurate than supervised learning for all tasks.

Answer

It is difficult to implement and requires specialized expertise.

Question 11

Which application of unsupervised learning is commonly used to identify fraudulent transactions in financial datasets?

Accepted Answer

Anomaly detection

Answer

Dimensionality reduction

Answer

Clustering

Answer

Regression

Question 12

Which application of unsupervised learning is typically used to identify customer segments in marketing datasets?

Accepted Answer

Clustering

Answer

Classification

Answer

Anomaly detection

Answer

Dimensionality reduction

Question 13

Which of the following algorithms is commonly used for unsupervised clustering tasks?

Accepted Answer

K-Means

Answer

Multilayer Perceptron

Answer

Random Forest

Answer

Linear Regression

Question 14

What is the primary objective of dimensionality reduction techniques?

Accepted Answer

Reducing the number of features in a dataset while retaining the most significant information

Answer

Removing outliers from a dataset

Answer

Increasing the number of features in a dataset

Question 15

Anomaly detection algorithms aim to identify:

Accepted Answer

Data points that deviate significantly from normal patterns

Answer

The most important features in a dataset

Answer

Linear relationships between features

Question 16

Hierarchical Agglomerative Clustering is based on which assumption?

Accepted Answer

Similar data points are closer to each other in a multidimensional space

Answer

The number of clusters in a dataset is known in advance

Answer

Data points can be represented as points in a Euclidean space

Question 17

Which algorithm aims to project high-dimensional data into a lower-dimensional space while preserving local relationships?

Accepted Answer

t-SNE (t-Distributed Stochastic Neighbor Embedding)

Answer

PCA (Principal Component Analysis)

Answer

LDA (Linear Discriminant Analysis)

Question 18

Which unsupervised learning technique is commonly used to identify patterns and structures in data without predefined labels?

Accepted Answer

Clustering

Answer

Supervised classification

Answer

Decision tree analysis

Answer

Linear regression

Question 19

Which measure is used to assess the quality of clustering algorithms?

Accepted Answer

Silhouette Coefficient

Answer

Precision

Answer

Recall

Answer

Accuracy

Question 20

What is the ultimate goal of anomaly detection algorithms?

Accepted Answer

Identifying data points that are significantly different from the rest of the data

Answer

Predicting future events based on historical data

Answer

Classifying data points into predefined categories

Question 21

Which of the following is NOT a type of clustering algorithm?

Accepted Answer

Supervised Clustering

Answer

Expectation-Maximization (EM) Clustering

Answer

Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

Answer

Hierarchical Clustering

Question 22

Which dimensionality reduction technique is used to find the principal components that explain the maximum variance in the data?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Independent Component Analysis (ICA)

Answer

Factor Analysis

Answer

Linear Discriminant Analysis (LDA)

Question 23

Which anomaly detection algorithm models the normal data distribution using a mixture of Gaussian distributions?

Accepted Answer

Gaussian Mixture Model (GMM)

Answer

One-Class Support Vector Machine (OC-SVM)

Answer

Isolation Forest

Answer

Local Outlier Factor (LOF)

Question 24

Which unsupervised learning algorithm aims to group similar data points together based on a measure of similarity or dissimilarity?

Accepted Answer

Clustering

Answer

Dimensionality Reduction

Answer

Association Rule Mining

Answer

Anomaly Detection

Question 25

Which dimensionality reduction technique preserves the neighborhood relationships between data points?

Accepted Answer

t-SNE (t-Distributed Stochastic Neighbor Embedding)

Answer

PCA

Answer

ICA

Answer

LDA

Question 26

Which anomaly detection algorithm uses a random forest of decision trees to identify data points that deviate significantly from the expected behavior?

Accepted Answer

Isolation Forest

Answer

OC-SVM

Answer

LOF

Answer

GMM

Question 27

Which type of unsupervised learning algorithm seeks to reduce the dimensionality of a dataset while preserving its most informative features?

Accepted Answer

Dimensionality Reduction

Answer

Clustering

Answer

Anomaly Detection

Answer

Association Rule Mining

Question 28

Which clustering algorithm uses a distance metric to iteratively assign data points to clusters?

Accepted Answer

K-Means Clustering

Answer

Agglomerative Clustering

Answer

Hierarchical Clustering

Answer

DBSCAN

Question 29

Which anomaly detection algorithm defines a threshold to identify data points that significantly deviate from the normal behavior?

Accepted Answer

Thresholding

Answer

LOF

Answer

GMM

Answer

OC-SVM

Question 30

Which dimensionality reduction technique aims to uncover the intrinsic dimensionality of a dataset by finding its underlying manifold structure?

Accepted Answer

Manifold Learning

Answer

LDA

Answer

ICA

Answer

PCA

Question 31

Which of the following is a type of unsupervised clustering algorithm?

Accepted Answer

K-means

Answer

Support vector machine

Answer

Logistic regression

Answer

Linear regression

Question 32

What is the primary goal of dimensionality reduction?

Accepted Answer

Reduce the number of features without significant information loss

Answer

Increase the number of features for better representation

Answer

Identify patterns and relationships in data

Question 33

Which type of unsupervised learning algorithm is used to detect unusual or unexpected data points?

Accepted Answer

Anomaly detection

Answer

Clustering

Answer

Regression

Answer

Dimensionality reduction

Question 34

K-means is a clustering algorithm that assigns data points to:

Accepted Answer

A fixed number of clusters

Answer

A variable number of clusters

Answer

Density-based clusters

Answer

Hierarchically organized clusters

Question 35

Which of the following is NOT a measure of cluster quality?

Accepted Answer

Root mean square error

Answer

Davies-Bouldin index

Answer

Calinski-Harabasz index

Answer

Silhouette coefficient

Question 36

Which of the following is a limitation of unsupervised learning?

Accepted Answer

Requires manually labeled data to train the model

Answer

Can handle large and complex datasets

Answer

Provides insights into the underlying structure of data

Question 37

Which type of clustering algorithm is suitable for data with varying densities?

Accepted Answer

Density-based clustering

Answer

Hierarchical clustering

Answer

K-means clustering

Answer

Spectral clustering

Question 38

Anomaly detection algorithms aim to identify data points that are:

Accepted Answer

Significantly different from the rest of the data

Answer

Representative of the average data point

Answer

Similar to the majority of the data

Answer

Close to the center of the data

Question 39

Which type of unsupervised learning algorithm aims to identify natural groupings or clusters within a dataset?

Accepted Answer

Clustering

Answer

Dimensionality reduction

Answer

Regression

Answer

Classification

Question 40

An unsupervised learning algorithm specifically designed to reduce the dimensionality of a dataset, making it easier to analyze and visualize, is known as:

Accepted Answer

Dimensionality reduction

Answer

Feature selection

Answer

Clustering

Answer

Classification

Question 41

Which type of unsupervised learning algorithm specializes in detecting unusual patterns or anomalies in a dataset?

Accepted Answer

Anomaly detection

Answer

Dimensionality reduction

Answer

Clustering

Answer

Regression

Question 42

Which unsupervised learning algorithm constructs a tree-like hierarchical structure to represent the relationships between data points?

Accepted Answer

Hierarchical clustering

Answer

Spectral clustering

Answer

DBSCAN

Answer

K-means clustering

Question 43

Which unsupervised learning algorithm is known for its computational efficiency in handling large datasets?

Accepted Answer

K-means clustering

Answer

Gaussian mixture models

Answer

Hierarchical clustering

Answer

Spectral clustering

Question 44

Which dimensionality reduction technique employs a linear combination of the original features to create a set of uncorrelated features?

Accepted Answer

Principal component analysis

Answer

Linear discriminant analysis

Answer

Factor analysis

Answer

T-distributed stochastic neighbor embedding

Question 45

Which type of unsupervised learning algorithm focuses on removing noise or unwanted variations from a dataset?

Accepted Answer

Denoising

Answer

Clustering

Answer

Dimensionality reduction

Answer

Feature selection

Question 46

A key advantage of unsupervised learning is:

Accepted Answer

It can be applied to data without labeled outcomes.

Answer

It requires significantly less data preparation than supervised learning.

Answer

It always provides higher accuracy than supervised learning.

Question 47

Which unsupervised learning algorithm aims to identify groups of similar data points?

Accepted Answer

Clustering

Answer

Dimensionality Reduction

Answer

Anomaly Detection

Answer

Supervised Learning

Question 48

Which unsupervised learning algorithm is specifically designed for reducing the dimensionality of data by identifying and preserving its most important features?

Accepted Answer

PCA (Principal Component Analysis)

Answer

K-Means

Answer

Logistic Regression

Answer

Isolation Forest

Question 49

Which unsupervised learning algorithm is utilized to detect data points that significantly deviate from the majority of the dataset?

Accepted Answer

Anomaly Detection

Answer

Regression

Answer

Reinforcement Learning

Answer

Classification

Question 50

Which metric is not used in K-Means clustering to calculate the distance between data points?

Accepted Answer

Cosine Similarity

Answer

Chebyshev Distance

Answer

Manhattan Distance

Answer

Euclidean Distance

Question 51

Which type of clustering algorithm constructs a hierarchical structure of clusters?

Accepted Answer

Hierarchical Clustering

Answer

Partitional Clustering

Answer

Grid-Based Clustering

Answer

Density-Based Clustering

Question 52

Which dimensionality reduction technique is particularly useful when dealing with high-dimensional and sparse data?

Accepted Answer

Random Projection

Answer

t-SNE

Answer

PCA

Answer

LDA

Question 53

Which clustering algorithm is suitable for handling large datasets due to its linear time complexity?

Accepted Answer

Streaming K-Means

Answer

DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

Answer

Hierarchical Clustering

Answer

Fuzzy C-Means

Question 54

Which unsupervised learning technique is used to learn the underlying structure of data without the need to explicitly define a target variable?

Accepted Answer

Unsupervised Mixture Model

Answer

Reinforcement Learning

Answer

Supervised Regression

Answer

Generative Adversarial Network (GAN)