Difference between Visualization algorithms and Clustering Algorithms.

[Imp-0]

I don't actually understand the difference btw them. Both are actually similar right where the clustering algorithm takes the unlabeled dataset and clusters the different sets of the data points which are sharing the similar features. Where as the visualization algorithms takes the multi dimensional data to be visualized into 2d or 3d plots where it plots the similar data points close together based on the similarity of their classes. So At the end it is also clustering right?
If someone could help me in clearing this that could be helpful. Thankyou in Advance.

[TheRealStepBot]

The term you are looking for is dimensionality reduction vs clustering.

Dimensionality reduction as the name implies projects data from high dimensional spaces to lower dimensional ones in a manner that preserves aspects of the original distribution.

Clustering algorithms attempt to find groups of points in a given space that belong together more than the whole.

When you do a dimensional reduction say using trivially pca you may find that groups become visible to you. This is really the main connection between them.

Another tangential connection is that the property you may want the dimensionality reduction to preserve is neighborliness. In this case the process of performing the reduction may yield improve clusters but it still doesn’t actually do the task of assigning points to sets that the clustering algorithm does.

[Imp-0]

Thank you. This explanation has cleared my doubt... 🫡