K-means for 2D point clustering in python

1. When to use k-means vs Dbscan?
2. Does Kmeans work with categorical data?
3. Can k-means be used for dimensionality reduction?

When to use k-means vs Dbscan?

K-means has difficulty with non-globular clusters and clusters of multiple sizes. DBSCAN is used to handle clusters of multiple sizes and structures and is not powerfully influenced by noise or outliers. K-means can be used for data that has a clear centroid, including a mean or median.

Does Kmeans work with categorical data?

The k-Means algorithm is not applicable to categorical data, as categorical variables are discrete and do not have any natural origin.

Can k-means be used for dimensionality reduction?

To Summarize, k-means can be used for a variety of purposes. We can use it to perform dimensionality reduction where each transformed feature is the distance of the point from a cluster center.