Mean Shift Clustering

Mean Shift Clustering is a method used to group data points based on their density in space. It works by iteratively shifting each point towards the average location (mean) of nearby points, effectively moving towards areas with higher data concentration. Over time, points converge into clusters around these dense regions, without needing to specify the number of clusters beforehand. This technique is useful for identifying natural groupings in data, especially when the shape and size of clusters are unknown. It’s widely applied in image analysis, pattern recognition, and other fields where understanding the inherent structure of data is important.