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Key or hash function should avoid clustering, the mapping of two or more keys to consecutive slots. Such clustering may cause the lookup cost to skyrocket, even if the load factor is low and collisions are infrequent. The popular multiplicative hash [1] is claimed to have particularly poor clustering behaviour. [2]
In computer programming, primary clustering is a phenomenon that causes performance degradation in linear-probing hash tables.The phenomenon states that, as elements are added to a linear probing hash table, they have a tendency to cluster together into long runs (i.e., long contiguous regions of the hash table that contain no free slots).
OCFS2, the Oracle Cluster File System was added [2] to the official Linux kernel with version 2.6.16, in January 2006. The alpha-quality code warning on OCFS2 was removed in 2.6.19. Red Hat's cluster software, including their DLM and GFS2 was officially added to the Linux kernel [3] with version 2.6.19, in November 2006.
Linear probing is a component of open addressing schemes for using a hash table to solve the dictionary problem.In the dictionary problem, a data structure should maintain a collection of key–value pairs subject to operations that insert or delete pairs from the collection or that search for the value associated with a given key.
We can test if a quotient filter contains some key, d, as follows. [4] We hash the key to produce its fingerprint, d H, which we then partition into its high-order q bits, d Q, which comprise its quotient, and its low-order r bits, d R, which comprise its remainder. Slot d Q is the key's canonical slot. That slot is empty if its three meta-data ...
Solr (pronounced "solar") is an open-source enterprise-search platform, written in Java.Its major features include full-text search, hit highlighting, faceted search, real-time indexing, dynamic clustering, database integration, NoSQL features [2] and rich document (e.g., Word, PDF) handling.
By Leah Douglas and Julie Steenhuysen (Reuters) -California's public health department reported a possible case of bird flu in a child with mild respiratory symptoms on Tuesday, but said there was ...
In data mining, k-means++ [1] [2] is an algorithm for choosing the initial values (or "seeds") for the k-means clustering algorithm. It was proposed in 2007 by David Arthur and Sergei Vassilvitskii, as an approximation algorithm for the NP-hard k-means problem—a way of avoiding the sometimes poor clusterings found by the standard k-means algorithm.