Using DBSCAN for clustering can be tough because of a few challenges:
Parameter Sensitivity: DBSCAN depends a lot on its settings, called parameters. One is , which is the maximum distance that two points can be apart to be seen as neighbors. The other is , the least number of points needed to form a group. Finding the right numbers for these can be hard.
Varying Densities: DBSCAN doesn’t work well when the groups in the data have different densities. This can cause clusters to mix together or points to be wrongly classified.
High Dimensionality: When dealing with high-dimensional data (data with many features), the idea of distance gets confusing. This makes it hard for DBSCAN to spot clusters accurately.
Using DBSCAN for clustering can be tough because of a few challenges:
Parameter Sensitivity: DBSCAN depends a lot on its settings, called parameters. One is , which is the maximum distance that two points can be apart to be seen as neighbors. The other is , the least number of points needed to form a group. Finding the right numbers for these can be hard.
Varying Densities: DBSCAN doesn’t work well when the groups in the data have different densities. This can cause clusters to mix together or points to be wrongly classified.
High Dimensionality: When dealing with high-dimensional data (data with many features), the idea of distance gets confusing. This makes it hard for DBSCAN to spot clusters accurately.