Which clustering algorithm does not require specifying the number of clusters beforehand?Hierarchical clusteringDBSCANK-MeansAgglomerative clustering

The clustering algorithm that does not require specifying the number of clusters beforehand is DBSCAN (Density-Based Spatial Clustering of Applications with Noise).

Explanation:

1. DBSCAN identifies clusters based on the density of data points in the space rather than needing a predetermined number of clusters. It groups together points that are closely packed together while marking as outliers points that lie alone in low-density regions.

2. In contrast:

   • K-Means requires the user to specify the number of clusters (K) before running the algorithm.
   • Hierarchical Clustering does start with either a predetermined number of clusters or can create a dendrogram from which clusters can be cut at the desired level, but this also relies on user input at some point.
   • Agglomerative Clustering is a specific type of hierarchical clustering that builds a hierarchy from individual points, which may later require a decision on the number of clusters when the dendrogram is formed.

In summary, DBSCAN is notably advantageous for those who wish to avoid specifying the number of clusters in advance.