What are the different clustering methods in data science?

What are the different clustering methods in data science?

There are several clustering methods, including K-means, hierarchical clustering, and DBSCAN. Would you like to know more about a specific one?

Can you explain K-means clustering?

K-means clustering partitions data into K distinct clusters based on feature similarity. It iteratively assigns points to the nearest cluster center.

What are the advantages of using hierarchical clustering?

Hierarchical clustering provides a tree-like structure of clusters, allowing for different levels of granularity and does not require specifying the number of clusters in advance.

Are there any limitations to DBSCAN?

Yes, DBSCAN can struggle with varying densities of clusters and may not perform well with high-dimensional data.