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  2. k-means clustering - Wikipedia

    en.wikipedia.org/wiki/K-means_clustering

    The filtering algorithm uses k-d trees to speed up each k-means step. [35] Some methods attempt to speed up each k-means step using the triangle inequality. [22] [23] [24] [36] [25] Escape local optima by swapping points between clusters. [9] The Spherical k-means clustering algorithm is suitable for textual data. [37]

  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-means algorithm - Wikipedia

    en.wikipedia.org/?title=K-means_algorithm&...

    K-means algorithm. Add languages. Add links. Article; Talk; ... Download QR code; Print/export Download as PDF; Printable version; In other projects

  5. Determining the number of clusters in a data set - Wikipedia

    en.wikipedia.org/wiki/Determining_the_number_of...

    The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]

  6. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Most k-means-type algorithms require the number of clusters – k – to be specified in advance, which is considered to be one of the biggest drawbacks of these algorithms. Furthermore, the algorithms prefer clusters of approximately similar size, as they will always assign an object to the nearest centroid.

  7. Data compression - Wikipedia

    en.wikipedia.org/wiki/Data_compression

    Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points. This process condenses extensive ...

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

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

    Download QR code; In other projects ... images showing the operation of the k-means algorithm. This is the fourth step (a repetition of the second step) where the ...

  9. File:K Means Example Step 2.svg - Wikipedia

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

    Download QR code ; In other projects ... This image is part of a series of images showing an example of the operation of the k-means algorithm. This is the second ...