- 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 QUESTIONS
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
Which distance metric is most commonly used in K-means clustering?