K Means Clustering:
K means clustering is a popular unsupervised learning algorithm, the goal of K means is to group similar data points and discover the underlying patterns in the data. In k means clustering, The algorithm starts by randomly initializing K centroids which is defined by the user. Each data points is assigned to the cluster with the closes centroid, the centroid is adjusted and repeats the process assigning to the cluster with closes centroid. This process is repeated until the centroids are no longer change or maximum number of iterations have reached.
DB Scan Clustering:
DB Scan clustering is one the popular unsupervised machine learning algorithm for understanding the underlying patterns in the data. DB Scan Clustering Algorithm groups the data points based on their density and proximity to each other. It is based on the idea that data points that are close together in space are morel likely to belong to the same category.
Example working of DB Scan Clustering Algorithm:
If we have group of people in room and if we ant to group people into different groups based on how close they are to each other, At first, we will have to specify two parameters one is Eps, the maximum distance between two people for them to be considered neighbors and MinPts, the minimum number of neighbors a person must have to be considered a core point. DB Scan identifies all the core points in the room. Then it iteratively expands the clusters by adding all non core points and their neighbors, including border points. In summary it is a powerful tool for clustering data.