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  2. Automatic clustering algorithms - Wikipedia

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

    BIRCH (balanced iterative reducing and clustering using hierarchies) is an algorithm used to perform connectivity-based clustering for large data-sets. [7] It is regarded as one of the fastest clustering algorithms, but it is limited because it requires the number of clusters as an input.

  3. List of cluster management software - Wikipedia

    en.wikipedia.org/wiki/List_of_cluster_management...

    ClusterVisor, [2] from Advanced Clustering Technologies [3] CycleCloud, from Cycle Computing acquired By Microsoft; Komodor, Enterprise Kubernetes Management Platform; Dell/EMC - Remote Cluster Manager (RCM) DxEnterprise, [4] from DH2i [5] Evidian SafeKit; HPE Performance Cluster Manager - HPCM, from Hewlett Packard Enterprise Company; IBM ...

  4. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    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).

  5. Comparison of cluster software - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_cluster_software

    Table Explanation. Software: The name of the application that is described; SMP aware: . basic: hard split into multiple virtual host; basic+: hard split into multiple virtual host with some minimal/incomplete communication between virtual host on the same computer

  6. Carrot2 - Wikipedia

    en.wikipedia.org/wiki/Carrot2

    Carrot² [1] is an open source search results clustering engine. [2] ... In the same year, version 2.0 was released with improved user interface and extended tool set.

  7. Hierarchical clustering - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_clustering

    The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of () and requires () memory, which makes it too slow for even medium data sets. . However, for some special cases, optimal efficient agglomerative methods (of complexity ()) are known: SLINK [2] for single-linkage and CLINK [3] for complete-linkage clusteri

  8. Locality-sensitive hashing - Wikipedia

    en.wikipedia.org/wiki/Locality-sensitive_hashing

    In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.

  9. UPGMA - Wikipedia

    en.wikipedia.org/wiki/UPGMA

    UPGMA (unweighted pair group method with arithmetic mean) is a simple agglomerative (bottom-up) hierarchical clustering method. It also has a weighted variant, WPGMA, and they are generally attributed to Sokal and Michener.