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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.
Availability Every request received by a non-failing node in the system must result in a response. This is the definition of availability in CAP theorem as defined by Gilbert and Lynch. [1] Note that availability as defined in CAP theorem is different from high availability in software architecture. [5] Partition tolerance
DKPro UBY is a Java framework for creating and accessing sense-linked lexical resources in accordance with the UBY-LMF lexicon model. While the code of UBY is licensed under a mix of free licenses such as GPL and CC by SA , some of the included resources are under different licenses such as academic use only .
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]
The JDeveloper code editor offers a rich set of coding features including visual and non-visual utilities that provide different views of the code. The software provides dialogs that guide the use of Java EE components. For example, JDeveloper provides a visual WYSIWYG editor for HTML, JSP, JSF, and Swing. The visual editor allows developers to ...
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).
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.
CURE (no. of points,k) Input : A set of points S Output : k clusters For every cluster u (each input point), in u.mean and u.rep store the mean of the points in the cluster and a set of c representative points of the cluster (initially c = 1 since each cluster has one data point).