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  2. Model-based clustering - Wikipedia

    en.wikipedia.org/wiki/Model-based_clustering

    Model-based clustering was first invented in 1950 by Paul Lazarsfeld for clustering multivariate discrete data, in the form of the latent class model. [ 41 ] In 1959, Lazarsfeld gave a lecture on latent structure analysis at the University of California-Berkeley, where John H. Wolfe was an M.A. student.

  3. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Download as PDF; Printable version; In other projects ... to precisely model this kind of data. Mean-shift is a clustering approach where each ... data mining Cluster ...

  4. k-means clustering - Wikipedia

    en.wikipedia.org/wiki/K-means_clustering

    Cluster analysis, a fundamental task in data mining and machine learning, involves grouping a set of data points into clusters based on their similarity. k -means clustering is a popular algorithm used for partitioning data into k clusters, where each cluster is represented by its centroid.

  5. Category:Cluster analysis algorithms - Wikipedia

    en.wikipedia.org/wiki/Category:Cluster_analysis...

    Download as PDF; Printable version; ... Pages in category "Cluster analysis algorithms" ... Data stream clustering; DBSCAN; E.

  6. Biclustering - Wikipedia

    en.wikipedia.org/wiki/Biclustering

    Biclustering, block clustering, [1] [2] Co-clustering or two-mode clustering [3] [4] [5] is a data mining technique which allows simultaneous clustering of the rows and columns of a matrix. The term was first introduced by Boris Mirkin [ 6 ] to name a technique introduced many years earlier, [ 6 ] in 1972, by John A. Hartigan .

  7. Data mining - Wikipedia

    en.wikipedia.org/wiki/Data_mining

    The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining).

  8. 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]

  9. 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.