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  2. Fuzzy clustering - Wikipedia

    en.wikipedia.org/wiki/Fuzzy_clustering

    Fuzzy clustering (also referred to as soft clustering or soft k-means) is a form of clustering in which each data point can belong to more than one cluster.. Clustering or cluster analysis involves assigning data points to clusters such that items in the same cluster are as similar as possible, while items belonging to different clusters are as dissimilar as possible.

  3. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Hard clustering: each object belongs to a cluster or not; Soft clustering (also: fuzzy clustering): each object belongs to each cluster to a certain degree (for example, a likelihood of belonging to the cluster) There are also finer distinctions possible, for example: Strict partitioning clustering: each object belongs to exactly one cluster

  4. 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.

  5. 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. This led Wolfe to think about how to do the ...

  6. HCS clustering algorithm - Wikipedia

    en.wikipedia.org/wiki/HCS_clustering_algorithm

    The HCS (Highly Connected Subgraphs) clustering algorithm [1] (also known as the HCS algorithm, and other names such as Highly Connected Clusters/Components/Kernels) is an algorithm based on graph connectivity for cluster analysis. It works by representing the similarity data in a similarity graph, and then finding all the highly connected ...

  7. Document clustering - Wikipedia

    en.wikipedia.org/wiki/Document_clustering

    Hard clustering computes a hard assignment – each document is a member of exactly one cluster. The assignment of soft clustering algorithms is soft – a document's assignment is a distribution over all clusters. In a soft assignment, a document has fractional membership in several clusters. [1]: 499 Dimensionality reduction methods can be ...

  8. Automatic clustering algorithms - Wikipedia

    en.wikipedia.org/.../Automatic_Clustering_Algorithms

    Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other cluster analysis techniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outlier points. [1] [needs context]

  9. Data structure - Wikipedia

    en.wikipedia.org/wiki/Data_structure

    A data structure known as a hash table.. In computer science, a data structure is a data organization and storage format that is usually chosen for efficient access to data. [1] [2] [3] More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data, [4] i.e., it is an algebraic structure about data.

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