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  2. k-means clustering - Wikipedia

    en.wikipedia.org/wiki/K-means_clustering

    The unsupervised k-means algorithm has a loose relationship to the k-nearest neighbor classifier, a popular supervised machine learning technique for classification that is often confused with k-means due to the name.

  3. Unsupervised learning - Wikipedia

    en.wikipedia.org/wiki/Unsupervised_learning

    Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. [1] Other frameworks in the spectrum of supervisions include weak- or semi-supervision , where a small portion of the data is tagged, and self-supervision .

  4. List of text mining methods - Wikipedia

    en.wikipedia.org/wiki/List_of_text_mining_methods

    Centroid-based Clustering: Unsupervised learning method. Clusters are determined based on data points. [1] Fast Global KMeans: Made to accelerate Global KMeans. [2] Global-K Means: Global K-means is an algorithm that begins with one cluster, and then divides in to multiple clusters based on the number required. [2]

  5. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Variations of k-means often include such optimizations as choosing the best of multiple runs, but also restricting the centroids to members of the data set (k-medoids), choosing medians (k-medians clustering), choosing the initial centers less randomly (k-means++) or allowing a fuzzy cluster assignment (fuzzy c-means).

  6. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    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 datasets into a more compact set of representative points.

  7. 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. [5]

  8. Automatic clustering algorithms - Wikipedia

    en.wikipedia.org/.../Automatic_Clustering_Algorithms

    Another method that modifies the k-means algorithm for automatically choosing the optimal number of clusters is the G-means algorithm. It was developed from the hypothesis that a subset of the data follows a Gaussian distribution. Thus, k is increased until each k-means center's data is Gaussian. This algorithm only requires the standard ...

  9. k-nearest neighbors algorithm - Wikipedia

    en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

    In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph Hodges in 1951, [1] and later expanded by Thomas Cover. [2] Most often, it is used for classification, as a k-NN classifier, the output of which is a class membership