
ML-DBSCAN

Quiz
•
Computers
•
University
•
Hard
KarunaiMuthu SriRam
Used 12+ times
FREE Resource
20 questions
Show all answers
1.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a clustering algorithm used in unsupervised machine learning that:
Maximizes the variance within clusters
Minimizes the distance between data points and cluster centroids
Groups data points based on their density and identifies noise points
Performs dimensionality reduction on the dataset
2.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
What is the main advantage of DBSCAN over traditional clustering algorithms like K-means?
DBSCAN can handle categorical data, while K-means cannot.
DBSCAN does not require specifying the number of clusters in advance.
DBSCAN guarantees convergence to the global optimum.
DBSCAN is computationally more efficient than K-means.
3.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
In DBSCAN, what are the two main parameters that need to be specified by the user?
Epsilon (ε) and the number of clusters (K)
Epsilon (ε) and the minimum number of points required to form a cluster (MinPts)
Learning rate and batch size
Mean and standard deviation of the dataset
4.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
How are data points categorized in DBSCAN?
Based on their distances from cluster centroids
Randomly assigned to clusters
Based on their density and proximity to other points
According to the mean and variance of the dataset
5.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
What is the role of the parameter "epsilon (ε)" in DBSCAN?
It defines the number of clusters to be formed.
It specifies the maximum distance between two data points for them to be considered part of the same cluster.
It represents the number of nearest neighbors required to form a cluster.
It determines the number of noise points in the dataset.
6.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
In DBSCAN, what are "core points"?
Data points that do not belong to any cluster
Data points located on the boundary between clusters
Data points with fewer than MinPts neighbors within ε distance
Data points that have more than MinPts neighbors within ε distance
7.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
What is the significance of the "MinPts" parameter in DBSCAN?
It determines the minimum number of clusters to be formed.
It specifies the maximum number of data points in each cluster.
It defines the minimum number of neighbors a data point must have to be considered a core point.
It represents the minimum distance between two clusters.
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