Practice Dbscan (density-based Spatial Clustering Of Applications With Noise) (6.1.2.3)

Practice: DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

Lesson 6.1.2.3Unsupervised Learning – Clustering & Dimensionality Reduction

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10 questions

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6 sample questions

Question 1.
What does DBSCAN stand for?
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Think about what each part of the acronym represents.
Question 2.
What are the two main parameters of DBSCAN?
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What does each parameter measure or define?
3Question 3.
What does DBSCAN primarily focus on for clustering?
- Distance
- Density
- Number of clusters
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Consider how points are grouped together.
4Question 4.
True or False: DBSCAN can only form spherical clusters.
- True
- False
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Think about the capabilities of density-based clustering.
Question 5.
In a dataset with five clusters of different densities, design a DBSCAN algorithm implementation. Describe how you would tune the parameters.
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Think about how density impacts clustering and potential adjustments.
Question 6.
Analyze the consequences of using a fixed ε value on a dataset with varying cluster densities. What issues could arise?
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Consider how changing densities influence the clustering outcome.

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