DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a powerful clustering algorithm widely used for identifying dense regions in data points. While DBSCAN offers several advantages, it is not without its challenges.
One significant challenge is the sensitivity to parameter settings, especially the epsilon and minimum points parameters. Selecting appropriate values for these parameters is crucial for the algorithm’s performance. If ε is too small, it may result in many data points being labeled as outliers, while a too-large ε might lead to merging distinct clusters. Similarly, the parameter influences the algorithm’s sensitivity to noise; setting it too high may cause smaller clusters to be disregarded.
DBSCAN can struggle with datasets of varying densities or irregularly shaped clusters. In cases where clusters have different levels of density, the algorithm may have difficulty identifying meaningful clusters. Additionally, clusters with complex geometries, such as elongated or hierarchical shapes, might pose challenges for DBSCAN, as the algorithm assumes clusters are dense, connected regions.
While DBSCAN is effective in discovering clusters of varying shapes and sizes, it is less suitable for handling datasets with outliers or noise. Outliers, especially those in sparse regions, may be incorrectly labeled as clusters, impacting the accuracy of the clustering results. DBSCAN’s performance is highly dependent on the underlying density distribution of the data, making it less effective in scenarios where clusters have varying densities