enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Cluster analysis is for example used to identify groups of schools or students with similar properties. Typologies From poll data, projects such as those undertaken by the Pew Research Center use cluster analysis to discern typologies of opinions, habits, and demographics that may be useful in politics and marketing.

  3. Mixture model - Wikipedia

    en.wikipedia.org/wiki/Mixture_model

    A multivariate Gaussian mixture model is used to cluster the feature data into k number of groups where k represents each state of the machine. The machine state can be a normal state, power off state, or faulty state. [6] Each formed cluster can be diagnosed using techniques such as spectral analysis.

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

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

    For example, in Gaussian radial basis function, it determines the dot product of the inputs in a higher-dimensional space, called feature space. It is believed that the data become more linearly separable in the feature space, and hence, linear algorithms can be applied on the data with a higher success.

  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. Automatic clustering algorithms - Wikipedia

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

    The Automatic Local Density Clustering Algorithm (ALDC) is an example of the new research focused on developing automatic density-based clustering. ALDC works out local density and distance deviation of every point, thus expanding the difference between the potential cluster center and other points.

  7. Dirichlet process - Wikipedia

    en.wikipedia.org/wiki/Dirichlet_process

    Animation of the clustering process for one-dimensional data using Gaussian distributions drawn from a Dirichlet process. The histograms of the clusters are shown in different colours. During the parameter estimation process, new clusters are created and grow on the data.

  8. k-means clustering - Wikipedia

    en.wikipedia.org/wiki/K-means_clustering

    [59]: 354, 11.4.2.5 This does not mean that it is efficient to use Gaussian mixture modelling to compute k-means, but just that there is a theoretical relationship, and that Gaussian mixture modelling can be interpreted as a generalization of k-means; on the contrary, it has been suggested to use k-means clustering to find starting points for ...

  9. Clustering high-dimensional data - Wikipedia

    en.wikipedia.org/wiki/Clustering_high...

    Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions.Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions ...