You've noticed that changing the Epsilon value drastically changes the clustering results in your DBSCAN model. What strategies could you employ to select an optimal value?
- Choose Epsilon randomly
- Set Epsilon to a fixed value across all datasets
- Use the 'k-distance graph'
- Use trial and error
The 'k-distance graph' is a common method used to select the optimal Epsilon value in DBSCAN. By plotting the distance to the kth nearest neighbor for each point, you can identify an inflection point that represents an optimal balance between cluster density and granularity, helping you to choose an appropriate Epsilon value.
Loading...
Related Quiz
- What is the principle behind the Random Forest algorithm?
- A dataset with very high between-class variance but low within-class variance is given. How would the LDA approach be beneficial here?
- Which technique involves setting a fraction of input units to 0 at each update during training time, which helps to prevent overfitting?
- For binary classification tasks, which regression outputs a probability score between 0 and 1?
- What is the primary purpose of using Cross-Validation in Machine Learning?