- Supervised Learning
- Classification
- Regression
- Time Series Forecasting
- Unsupervised Learning
- Clustering
- K-Means Clustering
- Hierarchical Clustering
- Semi-Supervised Learning
- Reinforcement Learning(ML)
- Deep Learning(ML)
- Transfer Learning(ML)
- Ensemble Learning
- Explainable AI (XAI)
- Bayesian Learning
- Decision Trees
- Support Vector Machines (SVMs)
- Instance-Based Learning
- Rule-Based Learning
- Neural Networks
- Evolutionary Algorithms
- Meta-Learning
- Multi-Task Learning
- Metric Learning
- Few-Shot Learning
- Adversarial Learning
- Data Pre Processing
- Natural Language Processing(ML)

# K-Means Clustering QUIZ - MCQ QUESTIONS AND ANSWERS

Question: 1

## How is the optimal number of clusters typically determined in K-means clustering?

Question: 2

## What is the primary assumption made by the K-means clustering algorithm?

Question: 3

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

Question: 4

## What is the main difference between K-means and K-medoids clustering algorithms?

Question: 5

## In K-means clustering, what is the role of the "inertia" or "within-cluster sum of squares"?

Question: 6

## How does K-means++ improve upon the original K-means algorithm?

Question: 7

## Which of the following is NOT an advantage of K-means clustering?

Question: 8

## How does the K-means clustering algorithm deal with an empty cluster?

Question: 9

## Which of the following is a disadvantage of the K-means clustering algorithm?

Question: 10

## What type of data does K-means clustering work best with?

Question: 11

## What is the time complexity of the K-means clustering algorithm?

Question: 12

## In K-means clustering, what is the purpose of the "elbow method"?

Question: 13

## Which of the following is a common application of K-means clustering?

Question: 14

## What happens if the number of specified clusters in K-means clustering is too small?

Question: 15

## What happens if the number of specified clusters in K-means clustering is too large?

Question: 16

## What is the difference between K-means clustering and hierarchical clustering?

Question: 17

## In K-means clustering, what does the term "convergence" refer to?

Question: 18

## Can K-means clustering handle non-convex clusters?

Question: 19

## Which of the following is a potential solution for dealing with categorical data in K-means clustering?

Question: 20

## In K-means clustering, how are initial centroids typically selected?

Question: 21

## How can K-means clustering be used for dimensionality reduction?

Question: 22

## In K-means clustering, which of the following factors can impact the quality of the clustering solution?

Question: 23

## How can K-means clustering be used for outlier detection?

Question: 24

## Which of the following is a limitation of K-means clustering in handling imbalanced datasets?

Question: 25

## Which of the following clustering algorithms can be used as an alternative to K-means clustering for handling categorical data?

Question: 26

## How can K-means clustering be extended to handle mixed-type data (both continuous and categorical)?

Question: 27

## In K-means clustering, what is the purpose of the silhouette score?

Question: 28

## In K-means clustering, which of the following techniques can be used to address the sensitivity to the initial placement of cluster centroids?

Question: 29

## What is the primary goal of K-means clustering?

Question: 30