The goal of k-means clustering is to find the clustering that minimizes the sum of the squared distances from each data item to its associated cluster mean. For example if a data item is (0.50, 0.80) ...

As Siddharth Dixit reflects, “ The beauty of density-based clustering lies in its ability to reveal hidden patterns without forcing data into predefined categories. It’s about letting the data ...