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
- You are developing a recommendation system for a music app. While the system's bias is low, it tends to offer very different song recommendations for slight variations in user input. This is an indication of which issue in the bias-variance trade-off?
- The regularization parameter 'C' in SVM controls the trade-off between maximizing the margin and minimizing the _________.
- When applying the K-Nearest Neighbors algorithm, scaling the features is essential because it ensures that each feature contributes __________ to the distance computation.
- Can you name a popular clustering algorithm used in Machine Learning?
- You're analyzing data from a shopping mall's customer behavior and notice that there are overlapping clusters representing different shopping patterns. To model this scenario, which algorithm would be most suitable?