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‘Clustering’ Interview Questions

What is Clustering?

What are the most common categories of clustering?

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

What are some common evaluation metrics in clustering?

How does K-Means Work?

What is Model-based Clustering?

What is Probabilistic (Fuzzy) Clustering?

What is Exclusive Clustering?

What is Hierarchical Clustering?

What is a Gaussian Mixture Model (GMM)?

How does K-Means ++ work?

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

What are some of the pros and cons of GMMs?

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

What is Expectation-Maximization (EM)?

What is KL Divergence?

What is Jaccard Index / Distance?

What is Cosine Similarity?

What is Minkowski Distance?

What is Manhattan Distance?

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

What loss function does K-Means seek to minimize?

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

What is Mahalanobis Distance?

What is Euclidean Distance?

What is Mutual Information (MI)?

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

What is Adjusted Rand Index (ARI)?

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

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

What is Rand Index?

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

What is Dunn Index?

What is Silhouette Score?

What is Within Cluster Sum of Squares (WCSS)?

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

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

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‘Clustering’ Interview Questions