Clustering Algorithms

This project implements two types of clustering algorithms, K-means and GMM.

• Data description: Four sets of data are given. The first two sets are simple low-dimensional data that can be directly visualized, and the last two sets are 128-dimensional high-dimensional data.
• In this project, I discussed and analyzed various situations such as the initialization mode of GMM, whether high-dimensional data needs dimensionality reduction and dimensionality reduction methods, and K-means convergence judgment, and conducted corresponding comparative experiments.
• The shortcoming of this project is that I did not give a comparative analysis with the results of directly calling the sk-learn library. If I have time later, I will make up for it.