Data Mining Methods(7)

 Bilal hussain Malik

                                                       Data Mining Methods

                                                      Dimensionality Reduction

Dimensionality reduction techniques reduce datasets by performing dimensionality decrease without sacrificing meaningful information. t-SNE and PCA are common algorithms that project high-dimensional data onto low-dimensional spaces. This is essential for visualizing data with high complexity, improving computational efficiency, and avoiding the "curse of dimensionality." Some applications include image compression, gene expression data analysis, and feature selection for machine learning. The process is used to reveal latent structure and patterns that may be obscured in high-dimensional data. Powerful, it needs to be used responsibly lest meaningful information be lost in reduction, and different algorithms may be needed depending on data characteristics and analysis goals.