LSI (Latent Semantic Indexing) is a mathematical technique used to identify relationships between a set of documents and the terms they contain. In the context of keyword clustering, LSI is used to identify groups of related keywords based on their semantic similarities, rather than just their literal meanings.
LSI works by creating a matrix of the relationships between the terms in a set of documents, and then performing a process called singular value decomposition (SVD) to identify the underlying latent semantic structure of the data. This allows LSI to identify clusters of related keywords that may not be immediately obvious based on their literal meanings.
By using LSI to cluster keywords, marketers and content creators can identify groups of related topics that can be used to optimize their content for search engines, improve their ad targeting, or better understand their audience’s interests and preferences.