Clustering Algorithms Overview

1. A silhouette coefficient value closer to 1 indicates what?

Poor clustering quality with overlapping clusters

Well-separated and cohesive clusters

The algorithm failed to converge

Equal distance between all cluster centers

2. What is the time complexity of k-means clustering for n data points, k clusters, and d dimensions?

O(n log n)

O(nkd)

O(n²)

O(d²)

3. Which preprocessing step is often essential before applying clustering algorithms?

Feature scaling and normalization

Creating dummy variables for all features

Removing all categorical variables

Increasing the number of features

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5. Which clustering algorithm is most suitable for discovering clusters of arbitrary shapes and sizes?

K-means

Gaussian Mixture Models

DBSCAN

Complete-linkage hierarchical clustering

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7. What is the Expectation-Maximization (EM) algorithm used for in clustering?

Computing distances between data points

Finding parameters of probabilistic clustering models

Normalizing feature values

Removing outliers from datasets

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9. What does the elbow method help determine in clustering?

The optimal number of clusters

The best distance metric to use

Whether data is normally distributed

The computational complexity of the algorithm

10. Which of the following is an internal clustering evaluation metric?

Purity score

Davies-Bouldin Index

Normalized Mutual Information

Adjusted Rand Index

11. What is the primary objective of clustering algorithms?

Group similar data points into clusters with minimal intra-cluster distance

Predict numerical values from input features

Classify data into predefined categories using labeled examples

Reduce dimensionality while preserving variance

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13. What is a key advantage of DBSCAN over k-means?

It requires specifying the number of clusters in advance

It can identify and handle outliers as noise

It converges faster for large datasets

It always produces spherical clusters

14. DBSCAN (Density-Based Spatial Clustering) requires two main parameters. Which pair is correct?

K and distance threshold

Epsilon and min_samples

Learning_rate and iterations

N_clusters and linkage method

15. What visualization method is commonly used to display hierarchical clustering results?

Scatter plot

Dendrogram

Histogram

Box plot

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17. Hierarchical clustering can be performed using two main approaches. Which is NOT one of them?

Agglomerative clustering

Divisive clustering

Iterative clustering

Both agglomerative and divisive are approaches

18. What is a potential limitation of k-means clustering?

It requires labeled training data

It is sensitive to the initial placement of centroids

It cannot handle numerical features

It always produces globally optimal results

19. Which distance metric is most commonly used in k-means clustering?

Manhattan distance

Euclidean distance

Cosine similarity

Hamming distance

20. In k-means clustering, what does the 'k' represent?

The number of features in the dataset

The number of clusters to form

The number of iterations required for convergence

The distance metric used

DataX Clustering Algorithms Overview Quiz

1. A silhouette coefficient value closer to 1 indicates what?

2.

What first name or nickname would you like us to use?

2. What is the time complexity of k-means clustering for n data points, k clusters, and d dimensions?

3. Which preprocessing step is often essential before applying clustering algorithms?

4. The curse of dimensionality affects clustering performance because ____.

5. Which clustering algorithm is most suitable for discovering clusters of arbitrary shapes and sizes?

6. In spectral clustering, the data is first transformed using ____.

7. What is the Expectation-Maximization (EM) algorithm used for in clustering?

8. Gaussian Mixture Models (GMM) assume that data is generated from a mixture of ____.

9. What does the elbow method help determine in clustering?

10. Which of the following is an internal clustering evaluation metric?

11. What is the primary objective of clustering algorithms?

12. The silhouette coefficient measures clustering quality by ____.

13. What is a key advantage of DBSCAN over k-means?

14. DBSCAN (Density-Based Spatial Clustering) requires two main parameters. Which pair is correct?

15. What visualization method is commonly used to display hierarchical clustering results?

16. In agglomerative hierarchical clustering, clusters are formed by ____.

17. Hierarchical clustering can be performed using two main approaches. Which is NOT one of them?

18. What is a potential limitation of k-means clustering?

19. Which distance metric is most commonly used in k-means clustering?

20. In k-means clustering, what does the 'k' represent?