Unsupervised Learning Algorithms Questions and Answers for Viva

Answer-1: Unsupervised learning is a type of machine learning where the model is trained using unlabeled data, and the algorithm tries to learn the underlying patterns, structures, or distribution in the data.

Answer-2: Common unsupervised learning algorithms include K-means clustering, hierarchical clustering, DBSCAN, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and autoencoders.

Answer-3: Clustering is a technique used in unsupervised learning to group similar data points together based on certain similarity measures. The most common algorithms used for clustering are K-means, DBSCAN, and hierarchical clustering.

Answer-4: K-means is a clustering algorithm that divides a set of data points into K clusters by minimizing the sum of squared distances between data points and the centroid of each cluster.

Answer-5: The number of clusters K in K-means can be determined using methods like the Elbow Method, Silhouette Score, or Gap Statistic. The Elbow Method involves plotting the within-cluster sum of squares for different values of K and choosing the K at which the decrease in error slows down.

Answer-6: The silhouette score is a metric used to evaluate the quality of a clustering. It measures how similar a point is to its own cluster compared to other clusters, with higher values indicating better clustering.

Answer-7: Hierarchical clustering is a clustering algorithm that builds a hierarchy of clusters by either merging small clusters into larger ones (agglomerative) or splitting larger clusters into smaller ones (divisive).

Answer-8: In hierarchical clustering, the number of clusters can be chosen by cutting the dendrogram at a specific level, or by using methods like the elbow method or silhouette score.

Answer-9: DBSCAN is a density-based clustering algorithm that groups together closely packed data points and labels points in low-density regions as noise. It is particularly useful for identifying clusters of arbitrary shapes.

Answer-10: K-means assumes that clusters are spherical and of similar sizes, whereas DBSCAN does not assume any specific cluster shape and can identify noise points. DBSCAN is better for datasets with clusters of varying densities.

Answer-11: PCA is a dimensionality reduction technique that transforms data into a set of linearly uncorrelated variables called principal components. These components capture the most variance in the data, helping to reduce the number of features while preserving important information.

Answer-12: PCA works by finding the eigenvectors and eigenvalues of the covariance matrix of the data, which represent the directions and magnitudes of the maximum variance in the data. The data is then projected onto these eigenvectors to form the principal components.

Answer-13: PCA is an unsupervised method for dimensionality reduction that focuses on maximizing variance, whereas LDA is a supervised method that seeks to maximize class separability by finding the directions that maximize the distance between classes.

Answer-14: The curse of dimensionality refers to the challenges that arise when working with high-dimensional data, such as increased computational cost, overfitting, and difficulties in visualizing the data.

Answer-15: t-SNE is a dimensionality reduction technique that is particularly useful for visualizing high-dimensional data by mapping it to a 2D or 3D space, while preserving local structures and relationships between data points.

Answer-16: Anomaly detection in unsupervised learning involves identifying data points that do not conform to the expected pattern or behavior, which can be useful for tasks like fraud detection, network security, and defect detection.

Answer-17: An autoencoder is a type of neural network used for unsupervised learning that learns to encode input data into a lower-dimensional representation and then reconstruct the input from this representation.

Answer-18: An autoencoder consists of an encoder that compresses the input into a latent space representation and a decoder that reconstructs the input from the compressed representation.

Answer-19: While both PCA and autoencoders are used for dimensionality reduction, PCA is a linear technique, whereas autoencoders are capable of learning non-linear transformations of the data.

Answer-20: ICA is a computational technique used to separate a multivariate signal into additive, independent components. It is commonly used for blind source separation, such as in audio signal processing.

Answer-21: A Gaussian Mixture Model is a probabilistic model that assumes the data is generated from a mixture of several Gaussian distributions. It is used for clustering and density estimation.

Answer-22: The number of components in a GMM can be selected using techniques like the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), or cross-validation.

Answer-23: The Expectation-Maximization (EM) algorithm is an iterative method used for parameter estimation in probabilistic models, such as Gaussian Mixture Models. It alternates between an expectation step (E-step) and a maximization step (M-step).

Answer-24: Dimensionality reduction helps reduce the number of features in a dataset while preserving important information, improving computational efficiency, and preventing overfitting.

Answer-25: In supervised learning, the model is trained using labeled data, whereas in unsupervised learning, the model is trained on unlabeled data and must find patterns or structures by itself.

Answer-26: A similarity measure is a metric used to quantify the similarity or dissimilarity between data points. Common similarity measures include Euclidean distance, cosine similarity, and Jaccard similarity.

Answer-27: The elbow method involves plotting the within-cluster sum of squares (WCSS) for different values of K and identifying the "elbow" point where the rate of decrease in WCSS slows down.

Answer-28: Selecting the right number of clusters is challenging because there is no ground truth in unsupervised learning. Techniques like the elbow method, silhouette score, and cross-validation can help, but the optimal number of clusters is often subjective.

Answer-29: Feature scaling ensures that all features contribute equally to the model. Without scaling, features with larger values dominate the clustering or dimensionality reduction process, leading to biased results.

Answer-30: Centroid-based clustering (e.g., K-means) relies on the center (centroid) of clusters to group points, while density-based clustering (e.g., DBSCAN) groups points based on the density of neighboring points, identifying areas of high density.

Answer-31: K-means is simple to implement, efficient with large datasets, and works well when clusters are spherical and of similar size. It also has a low computational complexity compared to other algorithms.

Answer-32: K-means is sensitive to the initial placement of centroids and may converge to local optima. It also struggles with clusters of non-spherical shapes and varying densities.

Answer-33: DBSCAN can identify noise points in the dataset by classifying them as outliers. Points that do not meet the minimum density requirement for a cluster are labeled as noise.

Answer-34: Agglomerative clustering starts with individual data points as separate clusters and iteratively merges them, while divisive clustering starts with one large cluster and iteratively splits it into smaller clusters.

Answer-35: The distance metric, such as Euclidean distance or Manhattan distance, determines how the similarity between data points is measured. The choice of distance metric can significantly impact the results of clustering.

Answer-36: Categorical features can be handled in clustering using methods like one-hot encoding, or by using distance metrics designed for categorical data, such as the Hamming distance or Gower's distance.

Answer-37: The gap statistic compares the total within intra-cluster variation for different numbers of clusters to the expected variation under a random distribution of points. It helps determine the optimal number of clusters.

Answer-38: PCA is sensitive to the scaling of features, assumes linear relationships between features, and can be affected by outliers. It may also discard important information if not carefully interpreted.

Answer-39: Probabilistic clustering algorithms, like Gaussian Mixture Models, assign a probability distribution to each data point for belonging to a particular cluster, allowing for soft clustering.

Answer-40: Density-based clustering algorithms, such as DBSCAN, group points based on their density in the data space. These algorithms can identify arbitrarily shaped clusters and handle noise better than centroid-based algorithms.

Answer-41: Applications of unsupervised learning include customer segmentation, anomaly detection, image compression, topic modeling, and reducing the dimensionality of large datasets.

Answer-42: Dimensionality reduction aims to reduce the number of features while preserving the important underlying structure of the data. It helps to improve computational efficiency and mitigate the curse of dimensionality.

Answer-43: DBSCAN is density-based and can find arbitrarily shaped clusters and detect noise, whereas K-means is centroid-based and assumes spherical clusters with a fixed size.

Answer-44: Yes, unsupervised learning can be used for feature selection. Methods like PCA and ICA help reduce the dimensionality by identifying the most important features, which can be useful for subsequent analysis.

Answer-45: t-SNE uses probability distributions to capture local relationships in high-dimensional space and maps them into lower dimensions, preserving both local and global structures.

Answer-46: Interpreting results in unsupervised learning often requires domain knowledge. Visualizations (e.g., scatter plots, dendrograms) and metrics like the silhouette score can provide insights into the quality of the clusters.

Answer-47: Feature selection involves selecting a subset of original features based on their importance, whereas dimensionality reduction transforms the features into a lower-dimensional space while preserving the important patterns.

Answer-48: A dendrogram is a tree-like diagram used to represent the arrangement of clusters formed during hierarchical clustering. It shows the merging or splitting of clusters at each level of the hierarchy.

Answer-49: High-dimensional data can be handled using dimensionality reduction techniques like PCA or t-SNE, which help to extract the most relevant features and make clustering or visualization more manageable.

Answer-50: In hard clustering, each data point is assigned to one and only one cluster, while in soft clustering (e.g., Gaussian Mixture Models), a data point can belong to multiple clusters with a certain probability.