University course assignment to model Density-based spatial clustering
data_clustering.csv is the dataset used, containing 200 two-dimensional feature vectors
This unsupervised clustering algorithm groups together data points that are packed ”densely” and identifies points that are positioned alone as ”outliers”. The main parameter settings for this function are MinPts(minimum points to form a cluster) and eps(the threshold value or radius when forming a cluster). The k-nearest neighbor search graph is used to find the most optimal eps value.