Question 11: Consider a dataset for which you applied DBSCAN with parameters
epsilon=0.5 and minPts=3. The dataset has points A, B, C, and D with distances:
d(A,B)=0.2, d(B,C)=0.6, d(A,C)=0.4, d(A,D)=0.7, d(B,D)=0.9, d(C,D)=0.3. Identify
core, border, and noise points. (10 points)

Question

Accepted Answer

***A*** ∻Key Concept∻ ⚹DBSCAN Core, Border, and Noise Points⚹ ∻Explanation∻ ⚹In DBSCAN, a core point has at least minPts within a distance of epsilon, a border point has fewer than minPts within epsilon but is in the neighborhood of a core point, and a noise point is neither a core nor a border point. Given epsilon=0.5 and minPts=3, point A has at least 3 points (B, C, D) within the epsilon distance, making it a core point. Point B has 2 points (A, C) within epsilon, making it a border point since it is adjacent to core point A. Point C, similar to B, is a border point. Point D, with only one point (C) within epsilon, is a noise point as it is not a core point nor adjacent to any core point.◊What is the formula to determine if a point is a core point in the DBSCAN algorithm based on the given parameters⍭ Generate me a similar question◊⚹

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