Related Data Science Links
Learn Dbscan Data Science Tutorial, validate concepts with Dbscan Data Science MCQ Questions, and prepare interviews through Dbscan Data Science Interview Questions and Answers.
DBSCAN Q&A
1What is DBSCAN?
Answer: Density-based clustering algorithm that groups dense regions and marks noise.
2Main parameters?
Answer: eps (neighborhood radius) and min_samples (minimum points).
3Core point?
Answer: Point with at least min_samples within eps radius.
4Border point?
Answer: Near core point but not dense enough to be core itself.
5Noise point?
Answer: Point not belonging to any dense cluster.
6DBSCAN vs K-Means?
Answer: DBSCAN finds arbitrary shapes and outliers; K-Means prefers spherical clusters.
7Need number of clusters beforehand?
Answer: No, DBSCAN infers clusters from density.
8How choose eps?
Answer: Use k-distance plot and domain-based scale understanding.
9Limitation?
Answer: Struggles when clusters have very different densities.
10One-line summary?
Answer: DBSCAN is great for irregular clusters and outlier detection.