K-Means Clustering Algorithm Basics

1. What is the computational complexity of a single K-means iteration?

O(n * k * d), where n is data points, k is clusters, d is dimensions

O(n^2)

O(log n)

O(k^2)

2. How can you address the instability of K-means results due to random initialization?

Run the algorithm multiple times with different initializations and select the best result

Use a fixed random seed only once

Increase the number of clusters

Manually specify all centroid positions

3. What assumption does K-means make about cluster shapes?

Clusters are roughly spherical with similar sizes

Clusters must be perfectly circular

Clusters can only be linear

Cluster shape is irrelevant

4. In K-means, what is the silhouette score used for?

Evaluating the quality and validity of cluster assignments

Determining the initial centroid positions

Calculating the distance between clusters

Removing duplicate data points

5. What is the update step in K-means?

Recalculate centroid positions based on assigned points

Reassign all data points to new clusters

Remove points that are far from centroids

Increase the number of clusters by one

6. How does K-means handle high-dimensional data?

It becomes less effective due to the curse of dimensionality

It automatically reduces dimensions

It performs identically to low-dimensional data

It requires no modifications

7. Which preprocessing step is often recommended before applying K-means?

Feature normalization or standardization

Removing all categorical variables

Increasing the number of features

Sorting the data in ascending order

8. Which problem can occur when using K-means with poorly chosen initial centroids?

Convergence to a local minimum instead of global minimum

The algorithm will never terminate

All points will be assigned to one cluster

The dataset becomes corrupted

9. What is the primary objective of the K-means clustering algorithm?

Minimize within-cluster variance and maximize between-cluster separation

Maximize the number of clusters in the dataset

Create equal-sized clusters regardless of data distribution

Identify outliers and remove them from the dataset

10. How is the distance typically measured between a data point and a centroid in K-means?

Euclidean distance

Manhattan distance

Cosine similarity

Hamming distance

11. What is the elbow method used for in K-means?

Determining the optimal number of clusters

Initializing centroid positions

Measuring cluster separation

Normalizing feature values

12. In K-means, when does the algorithm reach convergence?

When centroids no longer move significantly or assignments don't change

When all data points are equidistant from centroids

When the number of clusters equals the number of features

When the dataset is completely sorted

13. What happens during the assignment step of K-means?

Each data point is assigned to the nearest centroid

Centroids are moved to new random locations

Clusters are merged based on similarity

Outliers are removed from consideration

14. K-means is an example of which type of machine learning?

Unsupervised learning

Supervised learning

Reinforcement learning

Semi-supervised learning

15. What is a centroid in the context of K-means clustering?

The average position of all points assigned to a cluster

The point furthest from the cluster center

The point closest to all other points

The boundary line between clusters

16. Which method is most commonly used for initial centroid placement in K-means?

Random selection from the dataset

The geometric center of all data points

The median values of each feature

Hierarchical clustering results

17. In K-means, what does the 'K' represent?

The number of clusters to be created

The number of data points in the dataset

The number of features in the data

The number of iterations allowed

18. Which of the following is a limitation of K-means clustering?

It requires you to specify K in advance

It cannot handle numerical data

It always finds the global optimum solution

It is faster than all other clustering algorithms

19. Which algorithm is a variant of K-means designed for large datasets?

Mini-batch K-means

Hierarchical K-means

Fuzzy K-means

Weighted K-means

20. In K-means, what metric quantifies how well points fit within their assigned clusters?

Within-cluster sum of squares (WCSS)

Silhouette coefficient

Davies-Bouldin index

Dunn index

Data+ V1 K Means Clustering Algorithm Basics Quiz