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Questions and Answers
What is the main characteristic of DBSCAN algorithm?
What is the main characteristic of DBSCAN algorithm?
- It groups points based on their density within a specific radius (correct)
- It clusters points based on their similarity
- It is a centroid-based clustering algorithm
- It operates by dividing the data into a pre-defined number of clusters
What does the parameter 'Epsilon (eps)' determine in DBSCAN algorithm?
What does the parameter 'Epsilon (eps)' determine in DBSCAN algorithm?
- The minimum number of points within the neighborhood of a point for it to be considered a core point
- The radius around a point (correct)
- The maximum cluster size
- The distance between the centroids of clusters
What are 'Noise Points' in the context of DBSCAN algorithm?
What are 'Noise Points' in the context of DBSCAN algorithm?
- Points with at least a minPts number of points within a distance of eps
- Points that belong to the cluster of the core point they are connected to
- Points with varying density
- Points that do not satisfy the minPts condition (correct)
What is a limitation of DBSCAN clustering?
What is a limitation of DBSCAN clustering?
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What type of clusters does DBSCAN work best with?
What type of clusters does DBSCAN work best with?
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