FREE MS-DS Master of Data science Unsupervised Learning Techniques Questions and Answers

Question 1

A marketing team wants to segment its customer base into distinct groups based on purchasing behaviors (e.g., frequency, recency, monetary value) but has no predefined group labels for the customers.
Which unsupervised learning technique is most suitable for this task?

Accepted Answer

K-Means Clustering

Answer

Principal Component Analysis (PCA)

Answer

Linear Regression

Answer

Support Vector Machine (SVM)

Question 2

What is the primary objective of Principal Component Analysis (PCA) when used as an unsupervised learning technique?

Accepted Answer

To reduce dimensionality by finding a new set of uncorrelated variables (principal components) that capture the maximum variance in the data.

Answer

To classify data into predefined categories based on labeled examples.

Answer

To group data points into dense regions, identifying outliers as noise.

Answer

To predict a continuous target variable by fitting a linear equation to the observed data.

Question 3

Which of the following statements accurately describes a key difference between K-Means and DBSCAN clustering algorithms?

Accepted Answer

DBSCAN can identify noise points (outliers) that do not belong to any cluster, whereas K-Means assigns every point to a cluster.

Answer

K-Means is a density-based algorithm, while DBSCAN is a centroid-based algorithm.

Answer

K-Means is highly effective at identifying clusters of arbitrary shapes, unlike DBSCAN.

Answer

DBSCAN requires the user to specify the exact number of clusters to be found, while K-Means determines this automatically.

Question 4

A financial institution is analyzing a large dataset of credit card transactions to identify fraudulent activities. These fraudulent transactions are rare and often exhibit unusual patterns compared to the vast majority of legitimate transactions. Which type of unsupervised learning is most appropriate for this specific task?

Accepted Answer

Anomaly Detection

Answer

Association Rule Mining

Answer

Dimensionality Reduction

Answer

Hierarchical Clustering

Question 5

In agglomerative hierarchical clustering, what does the 'linkage criterion' (e.g., single, complete, average) determine?

Accepted Answer

The method for measuring the distance between clusters to decide which ones to merge next.

Answer

The number of initial clusters to be formed.

Answer

The distance metric used between individual data points, such as Euclidean or Manhattan distance.

Answer

The final shape of the clusters, forcing them to be spherical.

Question 6

A retail manager wants to analyze transaction data to discover which products are frequently purchased together, with the goal of optimizing store layout. For example, they want to find rules like "If a customer buys bread, they are 80% likely to also buy milk." Which unsupervised technique is best suited for this 'market basket analysis'?

Accepted Answer

Apriori Algorithm

Answer

t-SNE (t-Distributed Stochastic Neighbor Embedding)

Answer

K-Means Clustering

Answer

Principal Component Analysis (PCA)

A marketing team wants to segment its customer base into distinct groups based on purchasing behaviors (e.g., frequency, recency, monetary value) but has no predefined group labels for the customers.Which unsupervised learning technique is most suitable for this task?

A marketing team wants to segment its customer base into distinct groups based on purchasing behaviors (e.g., frequency, recency, monetary value) but has no predefined group labels for the customers.
Which unsupervised learning technique is most suitable for this task?