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  2. 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]

  3. Hierarchical clustering - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_clustering

    The basic principle of divisive clustering was published as the DIANA (DIvisive ANAlysis clustering) algorithm. [20] Initially, all data is in the same cluster, and the largest cluster is split until every object is separate. Because there exist () ways of splitting each cluster, heuristics are needed. DIANA chooses the object with the maximum ...

  4. Automatic clustering algorithms - Wikipedia

    en.wikipedia.org/wiki/Automatic_Clustering...

    The density-based clustering algorithm uses autonomous machine learning that identifies patterns regarding geographical location and distance to a particular number of neighbors. It is considered autonomous because a priori knowledge on what is a cluster is not required. [ 9 ]

  5. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters).

  6. Ward's method - Wikipedia

    en.wikipedia.org/wiki/Ward's_method

    Ward's minimum variance method can be defined and implemented recursively by a Lance–Williams algorithm. The Lance–Williams algorithms are an infinite family of agglomerative hierarchical clustering algorithms which are represented by a recursive formula for updating cluster distances at each step (each time a pair of clusters is merged).

  7. 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.

  8. Today’s NYT ‘Strands’ Hints, Spangram and Answers for Sunday ...

    www.aol.com/today-nyt-strands-hints-spangram...

    According to the New York Times, here's exactly how to play Strands: Find theme words to fill the board. Theme words stay highlighted in blue when found.

  9. Hierarchical clustering of networks - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_clustering_of...

    For this reason, their use in hierarchical clustering techniques is far from optimal. [1] Edge betweenness centrality has been used successfully as a weight in the Girvan–Newman algorithm. [1] This technique is similar to a divisive hierarchical clustering algorithm, except the weights are recalculated with each step.