Key Concepts

1. What is the main difference between partitioning and hierarchical clustering models?
2. Compare density-based clustering with distribution-based clustering. What are the primary differences in their approaches?
3. Define hard clustering and soft clustering, and give one example of each.
4. Why is feature selection important in cluster analysis, and how does dimensionality reduction help in this context?

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Measuring Similarity

1. How is Euclidean distance calculated between two points in a dataset?
2. When is Manhattan distance preferred over Euclidean distance in measuring similarity?
3. Explain how cosine similarity is different from Euclidean distance.
4. How does Pearson correlation measure similarity, and what kind of data is it best suited for?

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Clustering Algorithms

1. What are the steps involved in k-means clustering?
2. How does hierarchical clustering differ from k-means in terms of output and structure?
3. Describe how DBSCAN identifies clusters. What two parameters are critical for its functioning?
4. What is the role of Gaussian Mixture Models (GMMs) in clustering, and how do they differ from k-means?

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Validating Clusters