LSH Index
LSH (Locality-Sensitive Hashing) Index is a data structure and algorithmic technique used for approximate nearest neighbor search in high-dimensional spaces. It works by hashing similar input items into the same 'buckets' with high probability, enabling efficient similarity search by reducing the number of comparisons needed. It is commonly applied in tasks like image retrieval, recommendation systems, and duplicate detection where exact search is computationally expensive.
Developers should learn LSH Index when dealing with large-scale similarity search problems in high-dimensional data, such as in machine learning, data mining, or information retrieval applications. It is particularly useful for speeding up nearest neighbor queries in databases or search engines where precision can be traded for performance, making it ideal for real-time systems like content-based filtering or clustering algorithms.