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  2. OPTICS algorithm - Wikipedia

    en.wikipedia.org/wiki/OPTICS_algorithm

    It is a 2D plot, with the ordering of the points as processed by OPTICS on the x-axis and the reachability distance on the y-axis. Since points belonging to a cluster have a low reachability distance to their nearest neighbor, the clusters show up as valleys in the reachability plot. The deeper the valley, the denser the cluster.

  3. CURE algorithm - Wikipedia

    en.wikipedia.org/wiki/CURE_algorithm

    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).

  4. DBSCAN - Wikipedia

    en.wikipedia.org/wiki/DBSCAN

    DBSCAN optimizes the following loss function: [10] For any possible clustering = {, …,} out of the set of all clusterings , it minimizes the number of clusters under the condition that every pair of points in a cluster is density-reachable, which corresponds to the original two properties "maximality" and "connectivity" of a cluster: [1]

  5. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Download QR code; Print/export ... Cluster analysis or clustering is the task of grouping a ... it partitions the data space into a structure known as a Voronoi diagram.

  6. Automatic clustering algorithms - Wikipedia

    en.wikipedia.org/wiki/Automatic_Clustering...

    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]

  7. Nearest-neighbor chain algorithm - Wikipedia

    en.wikipedia.org/wiki/Nearest-neighbor_chain...

    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.

  8. Cluster diagram - Wikipedia

    en.wikipedia.org/wiki/Cluster_diagram

    A cluster in general is a group or bunch of several discrete items that are close to each other. The cluster diagram figures a cluster, such as a network diagram figures a network, a flow diagram a process or movement of objects, and a tree diagram an abstract tree. But all these diagrams can be considered interconnected: A network diagram can ...

  9. Availability - Wikipedia

    en.wikipedia.org/wiki/Availability

    series vs parallel components. Let's say a series component is composed of components A, B and C. Then following formula applies: Availability of series component = (availability of component A) x (availability of component B) x (availability of component C) [2] [3]