What are the criteria for a point to be considered a core point in DBSCAN?
- Being isolated from other clusters
- Being the central point of a cluster
- Being within Epsilon of at least MinPts other points
- Having the minimum distance to all other points in a cluster
A point is considered a core point in DBSCAN if it has at least MinPts other points within its Epsilon neighborhood radius. This means it's part of a dense region and is central to the formation of a cluster, connecting other core or border points.
Loading...
Related Quiz
- How does the curse of dimensionality impact the K-Nearest Neighbors algorithm, and what are some ways to address this issue?
- How does the choice of loss function affect the learning process in a Machine Learning model?
- The main advantage of Deep Q Networks over traditional Q-learning is their ability to handle high-dimensional ________ spaces.
- Which layer in a CNN is responsible for reducing the spatial dimensions of the input data?
- How can the Eigenvalues in PCA be used to determine the significance of the corresponding Eigenvectors?