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Carrot Search, [7] a commercial spin-off of the Carrot² project, works on further development of Carrot², offers a real-time text clustering algorithm [8] compliant with the Carrot² framework as well as text mining consulting services based on open source and proprietary software.
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).
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters).
Clustering software such as Solaris Cluster is a key component in a Business Continuity solution, and the Solaris Cluster Geographic Edition was created specifically to address that requirement. Solaris Cluster is an example of kernel-level clustering software.
C. Canopy clustering algorithm; Chinese whispers (clustering method) Cluster-weighted modeling; Cobweb (clustering) Complete-linkage clustering; ... Code of Conduct;
In the theory of cluster analysis, the nearest-neighbor chain algorithm is an algorithm that can speed up several methods for agglomerative hierarchical clustering.These are methods that take a collection of points as input, and create a hierarchy of clusters of points by repeatedly merging pairs of smaller clusters to form larger clusters.
Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other cluster analysis techniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outlier points. [1] [needs context]
In statistics and data mining, affinity propagation (AP) is a clustering algorithm based on the concept of "message passing" between data points. [1] Unlike clustering algorithms such as k-means or k-medoids, affinity propagation does not require the number of clusters to be determined or estimated before running the algorithm.