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  2. k-means clustering - Wikipedia

    en.wikipedia.org/wiki/K-means_clustering

    Smile contains k-means and various more other algorithms and results visualization (for java, kotlin and scala). Julia contains a k-means implementation in the JuliaStats Clustering package. KNIME contains nodes for k-means and k-medoids. Mahout contains a MapReduce based k-means. mlpack contains a C++ implementation of k-means. Octave contains ...

  3. k-means++ - Wikipedia

    en.wikipedia.org/wiki/K-means++

    In data mining, k-means++ [1] [2] is an algorithm for choosing the initial values (or "seeds") for the k-means clustering algorithm. It was proposed in 2007 by David Arthur and Sergei Vassilvitskii, as an approximation algorithm for the NP-hard k-means problem—a way of avoiding the sometimes poor clusterings found by the standard k-means algorithm.

  4. k-medoids - Wikipedia

    en.wikipedia.org/wiki/K-medoids

    Julia contains a k-medoid implementation of the k-means style algorithm (fast, but much worse result quality) in the JuliaStats/Clustering.jl package. KNIME includes a k-medoid implementation supporting a variety of efficient matrix distance measures, as well as a number of native (and integrated third-party) k-means implementations

  5. File:K Means Example Step 1.svg - Wikipedia

    en.wikipedia.org/wiki/File:K_Means_Example_Step...

    This image is part of an example of the K-means algorithm. This is the first step, where the points and centroids are randomly placed. Date: 26 July 2007: Source: Own ...

  6. File:K Means Example Step 4.svg - Wikipedia

    en.wikipedia.org/wiki/File:K_Means_Example_Step...

    Description: This image is part of a series of images showing the operation of the k-means algorithm. This is the fourth step (a repetition of the second step) where the data points are associated with their nearest centroids.

  7. Canopy clustering algorithm - Wikipedia

    en.wikipedia.org/wiki/Canopy_clustering_algorithm

    The canopy clustering algorithm is an unsupervised pre-clustering algorithm introduced by Andrew McCallum, Kamal Nigam and Lyle Ungar in 2000. [1] It is often used as preprocessing step for the K-means algorithm or the hierarchical clustering algorithm.

  8. File:K Means Example Step 3.svg - Wikipedia

    en.wikipedia.org/wiki/File:K_Means_Example_Step...

    This image is part of a series of images showing an example of the operation of the k-means algorithm. This is the third step where the centroids are moved to the average of all the data points. Date: 26 July 2007: Source: Own work: Author: Weston.pace

  9. Medoid - Wikipedia

    en.wikipedia.org/wiki/Medoid

    A common application of the medoid is the k-medoids clustering algorithm, which is similar to the k-means ... An implementation of ... Step 5. Steps 3-4 are repeated ...