Data Mining: Local Outlier Factor Explorer

This lab renders a synthetic dataset as a 3D point cloud — several dense clusters plus a handful of injected outliers — and runs the real Local Outlier Factor algorithm over it: for every point it finds the k nearest neighbours, computes a local reachability density from their reach-distances, and scores each point by how much sparser its neighbourhood is than its neighbours' neighbourhoods. Points are coloured by that score from blue (LOF ≈ 1, a normal point sitting in a neighbourhood as dense as its neighbours') through yellow to red (LOF far above 1, a genuine local outlier), the k slider changes how many neighbours define "local", the outlier-injection and cluster-spread sliders let you stress-test the algorithm against harder datasets, and clicking any point draws lines to its k-nearest neighbours while reporting its exact LOF score.