What is the primary goal of the K-Means Clustering algorithm?
- All of the Above
- Maximizing inter-cluster distance
- Minimizing intra-cluster distance
- Predicting new data points
The primary goal of K-Means is to minimize the intra-cluster distance, meaning the distance within the same cluster, to make the clusters as tight and well-separated as possible.
Loading...
Related Quiz
- When regular Q-learning takes too much time to converge in a high-dimensional state space (e.g., autonomous vehicle parking), what modification could help it learn faster?
- How do multi-class classification problems differ from binary classification problems?
- Which algorithm is based on the principle that similar data points are likely to have similar output values?
- A dataset contains both categorical and numerical features. Which ensemble method might be suitable, and what preprocessing might be required?
- Explain the concept of the bias-variance tradeoff in relation to overfitting and underfitting.