He uses density-based clustering algorithms like DBSCAN to find clusters of high-density data points within noisy datasets. His work is not just about processing data; it’s about uncovering the ...

Density-based clustering algorithms seek partitions with high density areas of points (clusters, not necessarily globular) separated by low density areas, possibly containing noise objects.

Then, several popular clustering techniques such as Agglomerative hierarchical clustering, K-means clustering algorithm, Partitioning around medoids, and Density-based clustering will be introduced.

Hierarchical Dirichlet Process (HDP) was considered to define groups of patients according to specific genomic features. 12 In addition, Hierarchical Density-Based Spatial Clustering of Applications ...