Abstract: The traditional K-means algorithm often leads to unstable clustering quality due to the randomness of the initial clustering center selection and tends to fall into suboptimal solutions when ...

Abstract: Computational effort is difficult when dealing with high dimensional data that has hundreds or thousands of features. Features that don't significantly influence class predictions throughout ...

AI content creation has exploded, creating a wave of auto-generated videos, scripts, and shows that compete with traditional programming. Live TV still holds power in news and sports, but audiences ...

This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the ...

This project used a Kmeans after PCA model to segment retail customers to optimize marketing efforts. When the model repeatedly returned a single cluster, the model was used to prove the customers' ...