During our class, we discussed the potential instability of DBSCAN in comparison to K-Means clustering. Here, we’ll outline various scenarios that illustrate the instability of DBSCAN:
Sensitivity to Density Variations:
DBSCAN’s stability can be affected by variations in data point density. If the dataset exhibits significant differences in data density across various segments, it can lead to the formation of clusters with varying sizes and shapes. Consequently, selecting appropriate parameters (such as the maximum distance ε and the minimum point thresholds) to define clusters effectively becomes a challenging task.
In contrast, K-Means assumes spherical and uniformly sized clusters, potentially performing more effectively when clusters share similar densities and shapes.
Sensitivity to Parameter Choices:
DBSCAN requires the configuration of hyperparameters, including ε (representing the maximum distance defining a data point’s neighborhood) and the minimum number of data points needed to establish a dense region. These parameter choices have a significant impact on the resulting clusters.
K-Means, while also requiring a parameter (the number of clusters, K), is generally more straightforward to determine, as it directly reflects the desired number of clusters. In contrast, DBSCAN’s parameters are more abstract, introducing sensitivity to the selection of parameter values.
Boundary Points and Noise:
DBSCAN explicitly identifies noise points, which are data points that don’t belong to any cluster, and it handles outliers well. However, the classification of boundary points (those located on the periphery of a cluster) within DBSCAN can sometimes appear arbitrary.
In K-Means, data points on the boundaries of clusters may be assigned to one of the neighboring clusters, potentially leading to instability when a data point is close to the shared boundary of two clusters.
Varying Cluster Shapes:
DBSCAN excels in its ability to accommodate clusters with arbitrary shapes and detect clusters with irregular boundaries. This is in contrast to K-Means, which assumes roughly spherical clusters and therefore demonstrates greater stability when data adheres to this assumption.
The choice between DBSCAN and K-Means should consider the specific characteristics of the dataset, as well as the objectives of the analysis, as these algorithms have different strengths and limitations.