Mean Shift is a powerful clustering algorithm used in unsupervised machine learning to identify dense regions within a dataset. Unlike traditional methods like K-Means, it does not require prior knowledge of the number of clusters, making it particularly useful for exploratory data analysis. The algorithm operates by iteratively shifting data points towards the mean of nearby points, effectively creating clusters around areas of high data density. This approach is widely applied in various fields, including image segmentation, object tracking, and pattern recognition, due to its ability to adaptively find clusters of varying shapes and sizes.