Disadvantages of HDBSCAN Clustering
- Computationally Intensive: HDBSCAN can be computationally expensive, particularly for large datasets, due to the construction of the minimum spanning tree and the calculation of mutual reachability distances.
- Sensitive to Distance metric: In HDBSCAN, the distance metric used can influence the clustering results. Some distance metrics may not accurately capture the data’s underlying structure, resulting in suboptimal clustering results.
- Parameter Sensitivity: Although HDBSCAN is less sensitive to parameter settings than some other clustering algorithms, it still requires parameter tuning, particularly for the minimum cluster size and minimum sample parameters, which can influence clustering results.
Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN)
Clustering is a machine-learning technique that divides data into groups, or clusters, based on similarity. By putting similar data points together and separating dissimilar points into separate clusters, it seeks to uncover underlying structures in datasets.
In this article, we will focus on the HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) technique. Like other clustering methods, HDBSCAN begins by determining the proximity of the data points, distinguishing the regions with high density from sparse regions. But what distinguishes HDBSCAN from other methods is its capacity to dynamically adjust to the different densities and forms of clusters in the data, producing more reliable and adaptable clustering results.