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In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.
MinHash. In computer science and data mining, MinHash (or the min-wise independent permutations locality sensitive hashing scheme) is a technique for quickly estimating how similar two sets are. The scheme was invented by Andrei Broder (1997), [1] and initially used in the AltaVista search engine to detect duplicate web pages and eliminate them ...
Feature hashing. In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features, i.e. turning arbitrary features into indices in a vector or matrix. [1][2] It works by applying a hash function to the features and using their hash values as indices ...
In computer science, the Rabin–Karp algorithm or Karp–Rabin algorithm is a string-searching algorithm created by Richard M. Karp and Michael O. Rabin (1987) that uses hashing to find an exact match of a pattern string in a text. It uses a rolling hash to quickly filter out positions of the text that cannot match the pattern, and then checks ...
Consistent hashing. In computer science, consistent hashing[1][2] is a special kind of hashing technique such that when a hash table is resized, only keys need to be remapped on average where is the number of keys and is the number of slots. In contrast, in most traditional hash tables, a change in the number of array slots causes nearly all ...
Universal hashing. In mathematics and computing, universal hashing (in a randomized algorithm or data structure) refers to selecting a hash function at random from a family of hash functions with a certain mathematical property (see definition below). This guarantees a low number of collisions in expectation, even if the data is chosen by an ...
SHA-2 (Secure Hash Algorithm 2) is a set of cryptographic hash functions designed by the United States National Security Agency (NSA) and first published in 2001. [3] [4] They are built using the Merkle–Damgård construction, from a one-way compression function itself built using the Davies–Meyer structure from a specialized block cipher.
Hash collision resolved by linear probing (interval=1). Open addressing, or closed hashing, is a method of collision resolution in hash tables.With this method a hash collision is resolved by probing, or searching through alternative locations in the array (the probe sequence) until either the target record is found, or an unused array slot is found, which indicates that there is no such key ...