What are the characteristics of the meanshift algorithm?

Characteristics of the Meanshift algorithm include:

1. Non-parametric: The Meanshift algorithm does not require assuming the distribution form of the data and can be applied to data distributions of any shape.
2. Adaptive: Meanshift algorithm automatically adjusts window size to adapt to changes in data density.
3. Unsupervised learning: The Meanshift algorithm can cluster data directly without requiring labeled training samples.
4. Global optimization: The Meanshift algorithm continuously adjusts the position of samples during an iterative process until it achieves the optimal clustering result.
5. Efficiency: The Meanshift algorithm efficiently finds the clustering centers of data by calculating local density and updating sample positions.
6. Meanshift algorithm has strong robustness to noisy data, being able to withstand a certain level of noise interference.