DBSCAN, which stands for Density-Based Spatial Clustering of Applications with Noise, is a clustering algorithm commonly used in data mining and machine learning. It’s designed to group together data points that are close to each other based on their density in the feature space. Here are the key components of DBSCAN:
Core Points: These are data points that have at least a specified number of data points (minPts) within a certain distance (eps) from them. Core points are at the heart of clusters.
Border Points: These points are within the epsilon distance of a core point but do not have enough neighbors to be considered core points themselves. They are on the fringes of clusters.
Noise Points: Data points that are neither core points nor border points are considered noise points. They don’t belong to any cluster.
The DBSCAN algorithm works as follows:
Randomly select an unvisited data point.
If it’s a core point, create a new cluster and add it to the cluster. Then expand the cluster by adding all directly reachable core points and their neighbors to the cluster.
Repeat steps 1 and 2 until all data points have been visited.
Any unvisited data points at this stage are classified as noise.
DBSCAN is effective at discovering clusters of arbitrary shapes and is robust to noise in the data. It doesn’t require specifying the number of clusters beforehand, which makes it a valuable tool for cluster analysis. However, setting the appropriate values for “eps” and “minPts” can be challenging, and the algorithm may not perform well in high-dimensional spaces due to the curse of dimensionality.