How does the choice of Epsilon affect the clustering results in DBSCAN?
- It affects the minimum points in a cluster
- It changes the clustering speed
- It determines the radius of the neighborhood around a point
- It modifies the clustering algorithm's underlying formula
The choice of Epsilon in DBSCAN determines the maximum radius of the neighborhood around a data point. By adjusting this value, one can control how close points must be to form a cluster, affecting the clustering's granularity, shape, and size. It's a crucial parameter to tune for achieving desired clustering results.
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?
- A model that makes decisions without being able to provide clear reasoning behind them lacks ________.
- Which variant of RNN is designed to better capture long-term dependencies in sequence data?
- When applying the K-Nearest Neighbors algorithm, scaling the features is essential because it ensures that each feature contributes __________ to the distance computation.
- In a fraud detection system, you have data with numerous features. You suspect that not all features are relevant, and some may even be redundant. Before feeding the data into a classifier, you want to reduce its dimensionality without losing critical information. Which technique would be apt for this?