Which clustering approach defines clusters as regions where density points exceed a threshold?

Which clustering approach defines clusters as regions where density points exceed a threshold?

• A. Agglomerative Clustering
• B. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
• C. Density-based Clustering
• D. SNN (Shared Nearest Neighbors)

Answer: (C)

Density-based clustering defines clusters as dense regions in which data points occur with high local density, separated by regions of lower density. The key idea is that a cluster is a neighborhood where points are packed together beyond a chosen density threshold, while sparser areas act as natural boundaries. In practice, you identify core points that have enough neighbors within a certain radius; those core points and all points density-connected to them form the same cluster, while points that don’t meet the density criteria are treated as noise. This description matches the statement about clusters being regions where density exceeds a threshold, which is the hallmark of density-based clustering. Agglomerative clustering relies on hierarchical merges based on distance or similarity rather than density thresholds, and SNN clusters by shared neighbor patterns rather than density itself. DBSCAN is a concrete algorithm that embodies this density-threshold idea, but the principle being tested is the density-based approach itself.

Density-based clustering defines clusters as dense regions in which data points occur with high local density, separated by regions of lower density. The key idea is that a cluster is a neighborhood where points are packed together beyond a chosen density threshold, while sparser areas act as natural boundaries. In practice, you identify core points that have enough neighbors within a certain radius; those core points and all points density-connected to them form the same cluster, while points that don’t meet the density criteria are treated as noise.

This description matches the statement about clusters being regions where density exceeds a threshold, which is the hallmark of density-based clustering. Agglomerative clustering relies on hierarchical merges based on distance or similarity rather than density thresholds, and SNN clusters by shared neighbor patterns rather than density itself. DBSCAN is a concrete algorithm that embodies this density-threshold idea, but the principle being tested is the density-based approach itself.