K-medoids are the most typical representatives in a group of data. They are selected so that the distance from each data point to the chosen medoid is as small as possible. To understand medoids, let’s compare them to central points in K-Means. The relationship between central points and medoids is similar to that of averages and middle values in a list. The key difference is that medoids and middle values are always actual data points, whereas central points and averages may not be. The main difference between K-Means and K-Medoids is how they group data. K-Means cluster the data based on the distances between data points and central points, while K-Medoids cluster it based on the distances to medoids. Unusual data points can influence k-means, but K-medoids are more resilient and don’t rely on central points, making them better at handling outliers.

DBSCAN is a clustering algorithm that identifies groups of data points that are close to each other, even if they do not have a circular or square shape. It can also detect data points that do not belong to any group. The algorithm works by measuring the distance between data points. If the distance is less than or equal to a predetermined value ε, the data points may be considered part of the same group. Additionally, a minimum number of data points, MinPts, must be within the distance ε for a group to be formed. Based on these criteria, DBSCAN classifies data points as Core Points, Border Points, or Outliers. Core Points have enough data points within the distance ε to form a cluster. Border Points can be reached from a Core Point but have fewer data points within the distance ε. Outliers are data points that do not belong to any group and cannot be reached from any Core Point.