ML-DBSCAN

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

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.

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

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

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.

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

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.

In DBSCAN, how are data points classified if they are not core points and do not have MinPts neighbors within ε distance?

• They are labeled as noise points.

• They are assigned to the cluster of the closest core point.

• They are removed from the dataset.

• They are assigned to the smallest cluster.

What is the time complexity of the DBSCAN algorithm for 'n' data points?

• O(n)

• O(n log n)

• O(n^2)

• O(n^3)

In DBSCAN, what is the purpose of the "border points"?

• They represent the boundaries of the dataset.

• They are data points with exactly MinPts neighbors within ε distance.

• They connect different clusters.

• They are core points that belong to multiple clusters.