Implementation of DBSCAN algorithm and comparing with Kmeans algorithm

This tutorial is about 'Implementation of DBSCAN algorithm and comparing with Kmeans algorithm'. A correction from video: Please replace the word 'Homogeneity' by 'Purity'. In this tutorial, I tried to explain some important concepts like: 1. How to determine 'eps' value for a given dataset. 2. How to calculate purity of a cluster. One thing I din't mention in the tutorial. The value of minPts depends on how many clusters you want to generate. Let's say if you want to generate big clusters and less number of clusters then set minPts value high. Too low value of minPts leads to generate more clusters from noise points so try to avoid setting minPts value too low. High or low value for minPts is relative and strongly depends on the size of the dataset. Find the 'optimal epsilon (Eps) value' paper here: http://iopscience.iop.org/article/10.1088/1755-1315/31/1/012012/pdf Find details about Normalization here: https://stats.stackexchange.com/questions/70801/how-to-normalize-data-to-0-1-range