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  2. 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 ...

  3. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Cluster analysis or clustering is the task of ... k-means clustering gives a formal definition as an ... by picking the top result from each cluster. [58] Slippy map ...

  4. k-means clustering - Wikipedia

    en.wikipedia.org/wiki/K-means_clustering

    If the data have three clusters, the 2-dimensional plane spanned by three cluster centroids is the best 2-D projection. This plane is also defined by the first two PCA dimensions. Well-separated clusters are effectively modelled by ball-shaped clusters and thus discovered by k-means. Non-ball-shaped clusters are hard to separate when they are ...

  5. Concept map - Wikipedia

    en.wikipedia.org/wiki/Concept_map

    A concept map or conceptual diagram is a diagram that depicts suggested relationships between concepts. [1] Concept maps may be used by instructional designers , engineers , technical writers , and others to organize and structure knowledge .

  6. Fuzzy clustering - Wikipedia

    en.wikipedia.org/wiki/Fuzzy_clustering

    Fuzzy clustering (also referred to as soft clustering or soft k-means) is a form of clustering in which each data point can belong to more than one cluster.. Clustering or cluster analysis involves assigning data points to clusters such that items in the same cluster are as similar as possible, while items belonging to different clusters are as dissimilar as possible.

  7. Hierarchical clustering - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_clustering

    In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories:

  8. Conceptual clustering - Wikipedia

    en.wikipedia.org/wiki/Conceptual_clustering

    Conceptual clustering is obviously closely related to data clustering; however, in conceptual clustering it is not only the inherent structure of the data that drives cluster formation, but also the Description language which is available to the learner.

  9. Cluster sampling - Wikipedia

    en.wikipedia.org/wiki/Cluster_sampling

    An example of cluster sampling is area sampling or geographical cluster sampling.Each cluster is a geographical area in an area sampling frame.Because a geographically dispersed population can be expensive to survey, greater economy than simple random sampling can be achieved by grouping several respondents within a local area into a cluster.