The provided Python files serve as a framework for implementing coreset construction algorithms and evaluating their performance: pip install numpy opencv-python scikit-learn scipy matplotlib streamlit
Skeleton.py: Provides a structure for implementing D2-sampling and coreset construction algorithms. Includes data preprocessing, parameter initialization, and performance evaluation steps.
Uniform_Sampling.py: Offers an alternative approach for constructing a coreset using uniform sampling. Randomly selects points from the dataset, assigns uniform weights, and applies KMeans clustering for evaluation.
wkpp.py: Contains the implementation of the kmeans_plusplus_w function, enabling weighted k-means++ initialization for coreset construction.
Next Steps: 1.Implement algorithms in Skeleton.py. 2.Test implemented algorithms with sample datasets. 3.Evaluate algorithm performance against KMeans clustering results.
Advanced Task (Bonus): Explore image segmentation using adapted coreset construction algorithms. Modify coreset construction for image data. Implement image segmentation using constructed coresets and compare results with original image segmentation