simple image clustring (university assignment)
In the first phase, attempt is to try 3 diffrent clustering methods over the ORL dataset: K-means, DBSACN & Agglomerative (Single Link, Complete Link, Group Average)
also, there is a basic implementation of "Rand Index" metric from scratch for result evaluating in Rand_index.py
In the second phase, we find a way to estimate epsilon parameter, in order to improve DBSACN clustering. we observe the average distance of a data to its 5 nearest nighboors. this average lead us to find a reasonable epsilon. We assume that this average distance is a good estimation for density distribution of dataset.