Mean Shift is a clustering algorithm used in data analysis to identify distinct groups within a dataset. It works by iteratively shifting data points towards the average of points in their neighborhood, effectively finding the densest areas of data. This process continues until the points converge, forming clusters based on their proximity. The algorithm is particularly useful in scenarios where the number of clusters is not known beforehand. Unlike other methods, such as K-means, Mean Shift does not require specifying the number of clusters in advance, making it adaptable to various types of data distributions.