High-Dimension Indexing

High-dimensional indexing involves organizing and searching data points that have many attributes or features, like images with numerous characteristics or documents with various keywords. Traditional indexing methods struggle because as the number of dimensions grows, data points become more spread out, making searches slower and less efficient—a phenomenon known as the "curse of dimensionality." High-dimensional indexing techniques use specialized structures, algorithms, or transformations to manage this complexity, enabling faster retrieval of relevant data even in complex, multi-attribute spaces. It’s essential in fields like machine learning, multimedia retrieval, and data science where handling multiple features efficiently is critical.