Understanding Core Data Science Algorithms: K-Means and K-Medoids Clustering - DZone Big Data

Clustering is one of the major techniques used for statistical data analysis. As the term suggests, "clustering" is defined as the process of gathering similar objects into different groups or distribution of datasets into subsets with a defined distance measure. K-means clustering is touted as a foundational algorithm every data scientist ought to have in their toolbox. K-means and k-medoids are methods used in partitional clustering algorithms whose functionality works based on specifying an initial number of groups or, more precisely, iteratively by reallocation of objects among groups. The algorithm works by first segregating all the points into an already selected number of clusters.