Free Data Science Unsupervised Learning Techniques Questions and Answers — Questions and Answers
Question 1: A data scientist is tasked with reducing the dimensionality of a large dataset containing highly correlated features. The goal is to create a smaller set of new, uncorrelated features that capture the maximum possible variance from the original data. Which unsupervised learning technique is most suitable for this purpose?
- K-Means Clustering
- Principal Component Analysis (PCA) (Correct answer)
- Apriori Algorithm
- DBSCAN
Correct answer: Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform a large set of variables into a smaller one that still contains most of the information. It works by creating new, uncorrelated variables, called principal components, that successively maximize the variance from the original data.
Question 2: A retail company wants to analyze customer transaction data to discover which products are frequently purchased together. This information will be used for product placement and promotional strategies. Which of the following algorithms is designed for this type of market basket analysis?
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Apriori Algorithm (Correct answer)
- K-Means Clustering
Correct answer: Apriori Algorithm
The Apriori algorithm is a classic algorithm used for association rule mining. It is designed to identify frequent itemsets in a dataset and generate association rules, making it ideal for market basket analysis to discover relationships between products in transactional data.
Question 3: A data science team is working with a dataset that has complex, non-spherical clusters and contains a significant amount of noise. The team does not know the number of clusters in advance. Which clustering algorithm would be the most effective choice for this scenario?
- K-Means Clustering
- Agglomerative Hierarchical Clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise) (Correct answer)
- Ward's Method
Correct answer: DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
DBSCAN is a density-based clustering algorithm that excels at finding arbitrarily shaped clusters and identifying noise points. Unlike K-Means, it does not require the number of clusters to be specified beforehand, making it suitable for exploratory analysis on complex datasets with outliers.
Question 4: Which of the following is a primary disadvantage of using hierarchical clustering algorithms, especially the agglomerative type, compared to K-Means clustering?
- It requires the number of clusters to be specified in advance.
- It is highly sensitive to the initial placement of centroids.
- It struggles with non-spherical or arbitrarily shaped clusters.
- It is computationally expensive and has high memory requirements for large datasets. (Correct answer)
Correct answer: It is computationally expensive and has high memory requirements for large datasets.
Hierarchical clustering algorithms, particularly agglomerative ones, have a higher time and space complexity (often O(n^3) in time and O(n^2) in space) compared to K-Means (which is closer to O(n)). This makes them less scalable and computationally intensive for large datasets.
Question 5: A data analyst is using an agglomerative hierarchical clustering algorithm and needs to decide how to measure the distance between clusters. They choose a method where the distance between two clusters is defined as the distance between the two closest points in the different clusters. What is this linkage criterion called?
- Complete Linkage
- Average Linkage
- Single Linkage (Correct answer)
- Ward's Linkage
Correct answer: Single Linkage
Single Linkage defines the distance between two clusters as the minimum distance between any single data point in the first cluster and any single data point in the second cluster. It is also known as the nearest-neighbor technique.
Question 6: In Principal Component Analysis (PCA), what does the first principal component represent?
- The direction in the feature space with the least variance.
- A randomly selected feature from the original dataset.
- The linear combination of original features that is most correlated with the target variable.
- The direction in the feature space along which the data has the maximum variance. (Correct answer)
Correct answer: The direction in the feature space along which the data has the maximum variance.
The first principal component (PC1) is defined as the orthogonal direction in the high-dimensional space that captures the maximum amount of variance in the data. Each subsequent principal component is orthogonal to the previous ones and captures the maximum remaining variance.
Free Data Science Unsupervised Learning Techniques Questions and Answers
A data scientist is tasked with reducing the dimensionality of a large dataset containing highly correlated features.
The goal is to create a smaller set of new, uncorrelated features that capture the maximum possible variance from the original data.
Which unsupervised learning technique is most suitable for this purpose?