What are some advanced clustering techniques in data science?

What are some advanced clustering techniques in data science?

Advanced clustering techniques include DBSCAN, hierarchical clustering, and Gaussian mixture models. Would you like to know more about any specific one?

Can you explain how DBSCAN works?

DBSCAN, or Density-Based Spatial Clustering of Applications with Noise, groups together points that are closely packed together while marking points in low-density regions as outliers. It requires two parameters: epsilon (the maximum distance between two samples for them to be considered as in the same neighborhood) and minPts (the minimum number of points required to form a dense region).

What are the advantages of using DBSCAN over K-means?

DBSCAN can find arbitrarily shaped clusters and is robust to outliers, unlike K-means which assumes spherical clusters and is sensitive to outliers. Additionally, DBSCAN does not require the number of clusters to be specified in advance.

Are there any limitations to using DBSCAN?

Yes, DBSCAN can struggle with clusters of varying densities and may not perform well in high-dimensional spaces due to the curse of dimensionality.