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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).
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]
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
NMF with the least-squares objective is equivalent to a relaxed form of K-means clustering: the matrix factor W contains cluster centroids and H contains cluster membership indicators. [15] [46] This provides a theoretical foundation for using NMF for data clustering. However, k-means does not enforce non-negativity on its centroids, so the ...
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.
This may improve the joins of these tables on the cluster key, since the matching records are stored together and less I/O is required to locate them. [2] The cluster configuration defines the data layout in the tables that are parts of the cluster. A cluster can be keyed with a B-tree index or a hash table. The data block where the table ...
The minimum disagreement correlation clustering problem is the following optimization problem: + + (). Here, the set + contains the attractive edges whose endpoints are in different components with respect to the clustering and the set () contains the repulsive edges whose endpoints are in the same component with respect to the clustering .
Common exceptions include an invalid argument (e.g. value is outside of the domain of a function), [5] an unavailable resource (like a missing file, [6] a network drive error, [7] or out-of-memory errors [8]), or that the routine has detected a normal condition that requires special handling, e.g., attention, end of file. [9]