Unsupervised Machine Learning Interview Questions

Checkout Vskills Interview questions with answers in Unsupervised Machine Learning to prepare for your next job role. The questions are submitted by professionals to help you to prepare for the Interview.

Q.1 What is unsupervised machine learning?

Unsupervised machine learning involves training models on data without labeled responses, aiming to identify patterns, relationships, or structures within the data, such as clustering or dimensionality reduction.

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Q.2 What is clustering, and name a few popular clustering algorithms?

Clustering is a technique that groups similar data points together based on certain features. Popular algorithms include K-Means, Hierarchical Clustering, and DBSCAN.

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Q.3 How does K-Means clustering work?

K-Means clustering partitions data into K clusters by minimizing the variance within each cluster. It iterates between assigning data points to the nearest centroid and updating centroids based on the mean of assigned points.

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Q.4 What is the elbow method in clustering?

The elbow method is used to determine the optimal number of clusters in K-Means by plotting the sum of squared distances from each point to its cluster center and identifying the "elbow" point where adding more clusters yields little improvement.

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Q.5 Explain Principal Component Analysis (PCA).

PCA is a dimensionality reduction technique that transforms data into a new coordinate system, reducing the number of variables while retaining as much variance as possible. It identifies principal components (directions of maximum variance).

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Q.6 What is the difference between PCA and t-SNE?

PCA is used for linear dimensionality reduction, preserving variance, while t-SNE is used for nonlinear dimensionality reduction, emphasizing preserving local structure and revealing complex patterns in data.

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Q.7 How does DBSCAN differ from K-Means?

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identifies clusters based on density and can find arbitrarily shaped clusters, while K-Means assumes spherical clusters and requires specifying the number of clusters.

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Q.8 What are outliers, and how can they affect unsupervised learning algorithms?

Outliers are data points that significantly differ from the majority of the data. They can skew results, affect cluster centers, and mislead dimensionality reduction algorithms, potentially leading to less accurate models.

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Q.9 What is hierarchical clustering, and how does it work?

Hierarchical clustering builds a hierarchy of clusters either through agglomerative (bottom-up) or divisive (top-down) approaches. Agglomerative starts with individual points and merges clusters, while divisive starts with one cluster and splits it.

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Q.10 How do you choose the number of clusters in clustering algorithms?

The number of clusters can be chosen using methods like the elbow method, silhouette score, or domain knowledge. Each method provides insights into how well-defined and useful the clusters are.

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Q.11 What is dimensionality reduction, and why is it important?

Dimensionality reduction reduces the number of features in a dataset while retaining important information. It is important for simplifying models, improving computational efficiency, and mitigating the curse of dimensionality.

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Q.12 Explain the concept of autoencoders in unsupervised learning.

Autoencoders are neural networks used for dimensionality reduction and feature learning. They consist of an encoder that compresses data into a latent space and a decoder that reconstructs the data from this compressed representation.

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Q.13 What is the difference between linear and nonlinear dimensionality reduction techniques?

Linear dimensionality reduction techniques, like PCA, assume that data can be represented in a linear subspace. Nonlinear techniques, like t-SNE and UMAP, capture more complex patterns and relationships that cannot be represented linearly.

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Q.14 How can you evaluate the performance of clustering algorithms?

Performance can be evaluated using metrics like silhouette score, Davies-Bouldin index, or by comparing the clustering results with known labels if available (e.g., using adjusted Rand index).

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Q.15 What is the role of feature scaling in unsupervised learning?

Feature scaling normalizes the range of features, ensuring that all features contribute equally to distance-based algorithms like K-Means and PCA. Without scaling, features with larger ranges can dominate the distance calculations.

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Q.16 Explain the concept of association rule mining.

Association rule mining discovers interesting relationships or associations between variables in large datasets. It is commonly used in market basket analysis to find items frequently purchased together.

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Q.17 What is the difference between supervised and unsupervised learning?

Supervised learning uses labeled data to train models and predict outcomes, while unsupervised learning uses unlabeled data to identify patterns, groupings, or structures within the data.

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Q.18 How do you handle missing values in unsupervised learning?

Handle missing values by techniques such as imputation (filling in missing values), removing records with missing data, or using algorithms that can handle missing values natively.

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Q.19 What are some common challenges in unsupervised learning?

Challenges include determining the right number of clusters, dealing with noisy or incomplete data, interpreting results without predefined labels, and ensuring the algorithms scale effectively with large datasets.

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Q.20 How do you interpret the results of unsupervised learning algorithms?

Interpret results by analyzing clusters or reduced dimensions to understand the underlying patterns, relationships, or structures. Visualization tools and domain knowledge can aid in making sense of the findings.

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Q.21 What are the key assumptions of K-Means clustering?

K-Means assumes that clusters are spherical, equally sized, and have a similar density. It also assumes that the number of clusters (K) is known beforehand.

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Q.22 How does the silhouette score help in evaluating clustering results?

The silhouette score measures how similar an object is to its own cluster compared to other clusters. Scores close to 1 indicate well-clustered data, while scores close to -1 suggest that the data might be assigned to the wrong cluster.

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Q.23 What is the role of the 'min_samples' parameter in DBSCAN?

The 'min_samples' parameter in DBSCAN defines the minimum number of data points required to form a dense region (i.e., a cluster). It helps in distinguishing between core points, border points, and noise.

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Q.24 How do you handle high-dimensional data in unsupervised learning?

Handle high-dimensional data using dimensionality reduction techniques like PCA or t-SNE to reduce the number of features while preserving important patterns or using feature selection methods to identify the most relevant features.

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Q.25 What is the concept of "mean shift" clustering?

Mean shift clustering is a non-parametric technique that assigns data points to the mode of the data distribution by iteratively shifting points towards the region of maximum data density.

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Q.26 How does hierarchical clustering handle different distances between clusters?

Hierarchical clustering can use different distance metrics, such as Euclidean or Manhattan distance, and linkage criteria (e.g., single, complete, or average linkage) to determine how clusters are merged or split.

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Q.27 What are some techniques for visualizing high-dimensional data?

Techniques include PCA, t-SNE, UMAP, and pairwise scatter plots. These methods help reduce dimensionality to visualize complex data structures in 2D or 3D space.

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Q.28 What is "Factor Analysis," and how is it different from PCA?

Factor Analysis is a technique used to identify underlying factors or latent variables that explain the correlations between observed variables. Unlike PCA, which focuses on variance, Factor Analysis models data in terms of latent variables.

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Q.29 How does UMAP differ from t-SNE in terms of computational efficiency?

UMAP is generally more computationally efficient than t-SNE, especially with large datasets, due to its optimization techniques and ability to preserve both local and global data structures more effectively.

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Q.30 What are the benefits and limitations of using autoencoders for dimensionality reduction?

Benefits include the ability to learn complex, nonlinear representations of data. Limitations include the need for careful tuning of architecture and potential overfitting if not enough data is available.

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Q.31 What is "Spectral Clustering," and how does it work?

Spectral Clustering uses eigenvalues of similarity matrices to reduce dimensionality before applying clustering algorithms like K-Means. It captures global data structure by transforming data into a space where clusters are more separable.

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Q.32 How does one select the right distance metric for clustering algorithms?

Select the distance metric based on the nature of the data and clustering goals. For example, Euclidean distance works well for spherical clusters, while Manhattan or cosine similarity might be more appropriate for other data distributions.

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Q.33 What is the difference between density-based and centroid-based clustering?

Density-based clustering (e.g., DBSCAN) groups points based on their density, allowing for arbitrary-shaped clusters and handling noise. Centroid-based clustering (e.g., K-Means) assigns points to clusters based on distance to centroids.

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Q.34 Explain the concept of "agglomerative" clustering.

Agglomerative clustering is a bottom-up approach where each data point starts as its own cluster, and pairs of clusters are merged iteratively based on their distance until a stopping criterion is met.

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Q.35 What is a "distance matrix," and how is it used in clustering?

A distance matrix is a table that shows the pairwise distances between data points. It is used in clustering algorithms like hierarchical clustering to determine how clusters are formed based on distance between points.

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Q.36 What is "Dimensionality Reduction with Non-Negative Matrix Factorization (NMF)?"

NMF is a dimensionality reduction technique that factors data into two non-negative matrices. It is often used for feature extraction and can be more interpretable when dealing with non-negative data.

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Q.37 What are "self-organizing maps," and how are they used in unsupervised learning?

Self-organizing maps (SOMs) are a type of neural network that uses unsupervised learning to produce a low-dimensional representation of data while preserving the topological structure. They are used for clustering and visualization.

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Q.38 How do you evaluate the stability of clusters?

Evaluate cluster stability by perturbing the data (e.g., through noise addition or sampling) and checking if the clustering results remain consistent. Stability measures the robustness and reliability of the clusters.

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Q.39 What is the role of "latent variables" in unsupervised learning?

Latent variables are hidden factors or features that are not directly observed but influence the observed data. They are often used in models like Factor Analysis or Bayesian methods to capture underlying patterns.

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Q.40 How do you handle mixed data types (e.g., numerical and categorical) in clustering?

Handle mixed data types by using distance metrics that can accommodate both types (e.g., Gower distance), or by preprocessing the data to convert categorical variables into numerical forms through encoding techniques.

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Q.41 What is the difference between a generative and a discriminative model in unsupervised learning?

Generative models (e.g., Gaussian Mixture Models) learn the distribution of data to generate new samples, while discriminative models (e.g., clustering algorithms) focus on distinguishing between different classes or clusters in the data.

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Q.42 Explain the concept of “local outlier factor” (LOF).

LOF is an anomaly detection algorithm that measures the local density of data points. Points with significantly lower density compared to their neighbors are considered outliers.

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Q.43 What are some common applications of clustering algorithms?

Applications include customer segmentation, anomaly detection, image segmentation, document clustering, and market basket analysis.

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Q.44 What is "Gaussian Mixture Model" (GMM), and how does it work?

GMM is a probabilistic model that assumes data is generated from a mixture of several Gaussian distributions with unknown parameters. It uses Expectation-Maximization (EM) to estimate the parameters and assign data points to clusters.

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Q.45 How does t-SNE handle the curse of dimensionality?

t-SNE handles the curse of dimensionality by converting high-dimensional distances into probabilities and then preserving these probabilities in a lower-dimensional space, making the data easier to visualize.

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Q.46 What is the "Silhouette Analysis," and how is it used in clustering?

Silhouette Analysis measures how similar an object is to its own cluster compared to other clusters. It provides a score between -1 and 1 to assess the quality of clustering.

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Q.47 What are some methods to handle imbalanced datasets in unsupervised learning?

Methods include resampling techniques (oversampling or undersampling), using synthetic data generation (e.g., SMOTE), and applying algorithms designed to handle imbalanced data.

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Q.48 How does the choice of the distance metric affect clustering results?

The choice of distance metric (e.g., Euclidean, Manhattan, cosine similarity) influences the shape and separation of clusters. Different metrics may lead to different clustering outcomes based on the data characteristics.

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Q.49 What is "Dimensionality Reduction with Singular Value Decomposition (SVD)?"

SVD is a matrix factorization technique that decomposes a matrix into three other matrices, capturing the most significant singular values and vectors. It is used for dimensionality reduction and feature extraction.

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Q.50 What is "Feature Engineering," and why is it important in unsupervised learning?

Feature engineering involves creating new features or transforming existing ones to improve model performance. In unsupervised learning, it helps in identifying meaningful patterns and enhancing clustering or dimensionality reduction.

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Q.51 What is the "k-Nearest Neighbors" (k-NN) algorithm used for in unsupervised learning?

In unsupervised learning, k-NN can be used for clustering (e.g., k-NN clustering) and anomaly detection by measuring the distance between data points to identify similar or dissimilar points.

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Q.52 Explain the concept of "Hierarchical Density-Based Spatial Clustering" (HDBSCAN).

HDBSCAN extends DBSCAN by using hierarchical clustering to handle varying cluster densities and to identify clusters with different densities more effectively.

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Q.53 What is "LDA" (Latent Dirichlet Allocation), and how is it used in unsupervised learning?

LDA is a generative probabilistic model used for topic modeling. It identifies topics in a collection of documents by discovering the underlying distribution of words in documents.

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Q.54 How does the “Gower Distance” handle mixed data types in clustering?

Gower Distance calculates the dissimilarity between data points by handling different data types (e.g., numerical, categorical) separately and then combining the results into a single distance metric.

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Q.55 What are “Cluster Validity Indices,” and why are they important?

Cluster validity indices measure the quality and validity of clustering results. Examples include the Davies-Bouldin index and Calinski-Harabasz index. They help in evaluating and comparing the effectiveness of clustering solutions.

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Q.56 Explain the concept of "Isolation Forest" for anomaly detection.

Isolation Forest is an anomaly detection algorithm that isolates anomalies by randomly selecting features and splitting values. Anomalies are isolated faster than normal observations, making them easier to detect.

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Q.57 What is the role of “mutual information” in feature selection for unsupervised learning?

Mutual information measures the amount of information obtained about one feature from another. In feature selection, it helps identify features that are most informative and relevant for clustering or dimensionality reduction.

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Q.58 How do you handle categorical variables in unsupervised learning algorithms?

Handle categorical variables by encoding them into numerical values using techniques like one-hot encoding, label encoding, or using distance metrics designed for mixed data types.

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Q.59 What is "Non-negative Matrix Factorization" (NMF), and how is it used?

NMF is a matrix factorization technique that decomposes a matrix into non-negative factors. It is used for feature extraction, dimensionality reduction, and pattern discovery, especially in text and image data.

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Q.60 How do you choose the appropriate unsupervised learning algorithm for a given problem?

Choose an algorithm based on data characteristics, such as the type of data (continuous, categorical), the desired outcome (clustering, dimensionality reduction), and computational resources. Understanding the strengths and weaknesses of different algorithms helps in selecting the most suitable one.

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