K Mean

K-means clustering is an unsupervised machine learning technique aiming to partition data into k distinct groups based on similarity, minimizing the within-cluster variance. Current research focuses on improving K-means' efficiency and robustness, particularly for high-dimensional and imbalanced datasets, through techniques like dimensionality reduction (e.g., PCA), enhanced initialization methods (e.g., k-means++), and the integration with other algorithms (e.g., self-supervised graph embedding, Bayesian bootstrapping). These advancements are impacting diverse fields, including speech enhancement, financial risk forecasting, and personalized recommendations, by enabling more accurate and efficient data analysis.