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  2. k-means clustering - Wikipedia

    en.wikipedia.org/wiki/K-means_clustering

    k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster.

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

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

    In statistics and data mining, X-means clustering is a variation of k-means clustering that refines cluster assignments by repeatedly attempting subdivision, and keeping the best resulting splits, until a criterion such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC) is reached.

  4. Microarray analysis techniques - Wikipedia

    en.wikipedia.org/wiki/Microarray_analysis_techniques

    K-means clustering is an algorithm for grouping genes or samples based on pattern into K groups. Grouping is done by minimizing the sum of the squares of distances between the data and the corresponding cluster centroid. Thus the purpose of K-means clustering is to classify data based on similar expression. [20]

  5. Model-based clustering - Wikipedia

    en.wikipedia.org/wiki/Model-based_clustering

    Several of these models correspond to well-known heuristic clustering methods. For example, k-means clustering is equivalent to estimation of the EII clustering model using the classification EM algorithm. [8] The Bayesian information criterion (BIC) can be used to choose the best clustering model as well as the number of clusters. It can also ...

  6. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    When the number of clusters is fixed to k, k-means clustering gives a formal definition as an optimization problem: find the k cluster centers and assign the objects to the nearest cluster center, such that the squared distances from the cluster are minimized.

  7. Computational biology - Wikipedia

    en.wikipedia.org/wiki/Computational_biology

    One example is k-means clustering, which aims to partition n data points into k clusters, in which each data point belongs to the cluster with the nearest mean. Another version is the k-medoids algorithm, which, when selecting a cluster center or cluster centroid, will pick one of its data points in the set, and not just an average of the cluster.

  8. 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. ... K-means clustering; Global ...

  9. 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.