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‘Unsupervised Learning’ Interview Questions

What is Unsupervised learning?

What is Clustering?

What are the most common categories of clustering?

What is dimensionality reduction?

What is Principal Component Analysis (PCA), and how does it differ from clustering?

What is Exclusive Clustering?

What is Probabilistic (Fuzzy) Clustering?

How does K-Means Work?

What is Hierarchical Clustering?

What is a Gaussian Mixture Model (GMM)?

What is Expectation-Maximization (EM)?

What is Model-based Clustering?

How does DBSCAN Clustering work, and in what cases is it useful?

What is Spectral Clustering?

What is Spectral co-clustering?

What is Bi-Clustering? What are possible use cases of it?

How does K-Means ++ work?

What is Kernel PCA?

How does imposing connectivity constraints help with Agglomerative clustering?

What are some options for clustering on categorical data? What if the dataset contains a combination of numeric and categorical features?

How is clustering affected by high-dimensional data, and how can the quality of clusters generated be improved in such cases?

How does the initial choice of centroids affect the K-Means algorithm?

How do outliers affect the clusters formed in K-Means?

What are some common evaluation metrics in clustering?

What are some common distance metrics that can be used in clustering?

How does the EM algorithm (in the context of GMM) compare to K-Means?

What are some of the pros and cons of hierarchical clustering compared to K-Means?

What are some of the pros and cons of GMMs?

What are some pros and cons of K-Means Clustering?

What is Within Cluster Sum of Squares (WCSS)?

What is Silhouette Score?

What is Dunn Index?

What is Rand Index?

What is Adjusted Rand Index (ARI)?

What is Mutual Information (MI)?

What is Euclidean Distance?

What is Mahalanobis Distance?

What is Manhattan Distance?

What is Minkowski Distance?

What is Cosine Similarity?

What is Jaccard Index / Distance?

What is KL Divergence?

How can you choose the optimal value for ‘k’ in K-Means?

What loss function does K-Means seek to minimize?

What is the effect of minimizing the within-cluster sum of squares on the shapes of clusters produced in K-Means?

What are the two ways in which Hierarchical clustering can proceed?

What are some of the possible linkage types to use in order to form successive clusters?

What is a dendrogram, and how is it used in hierarchical clustering?

What are some options for identifying the number of components in a GMM?

When to use PCA vs Random Projection?

What is Random Projection? Discuss its advantages and disadvantages?

How does T-SNE compare to PCA?

How does T-distributed Stochastic Neighbor Embedding (T-SNE) work at a high level?

What is Factor Analysis, and how does it differ from PCA?

What is Independent Component Analysis (ICA), and how is it distinguished from PCA?

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