What are the different clustering algorithms in machine learning?

What are the different clustering algorithms in machine learning?

There are several clustering algorithms, including K-means, hierarchical clustering, DBSCAN, and Gaussian mixture models. Each has its own strengths and weaknesses.

Can you explain how K-means works?

K-means works by partitioning data into K clusters. It initializes K centroids, assigns data points to the nearest centroid, and then updates the centroids based on the assigned points. This process repeats until convergence.

What is the difference between K-means and hierarchical clustering?

K-means is a partitioning method that requires the number of clusters to be specified in advance, while hierarchical clustering builds a tree of clusters without needing to predefine the number of clusters.

When should I use DBSCAN instead of K-means?

DBSCAN is preferable when dealing with clusters of varying shapes and sizes, and it can also identify noise in the data, unlike K-means which assumes spherical clusters.