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Agglomerative Algorithm

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David S Matteson – One of the best experts on this subject based on the ideXlab platform.

  • ecp: AnRPackage for Nonparametric Multiple Change Point Analysis of Multivariate Data
    Journal of Statistical Software, 2014
    Co-Authors: Nicholas A James, David S Matteson
    Abstract:

    There are many different ways in which change point analysis can be performed, from purely parametric methods to those that are distribution free. The ecp package is designed to perform multiple change point analysis while making as few assumptions as possible. While many other change point methods are applicable only for univariate data, this R package is suitable for both univariate and multivariate observations. Hierarchical estimation can be based upon either a divisive or Agglomerative Algorithm. Divisive estimation sequentially identifies change points via a bisection Algorithm. The Agglomerative Algorithm estimates change point locations by determining an optimal segmentation. Both approaches are able to detect any type of distributional change within the data. This provides an advantage over many existing change point Algorithms which are only able to detect changes within the marginal distributions

  • ecp an r package for nonparametric multiple change point analysis of multivariate data
    arXiv: Computation, 2013
    Co-Authors: Nicholas A James, David S Matteson
    Abstract:

    There are many different ways in which change point analysis can be performed, from purely parametric methods to those that are distribution free. The ecp package is designed to perform multiple change point analysis while making as few assumptions as possible. While many other change point methods are applicable only for univariate data, this R package is suitable for both univariate and multivariate observations. Estimation can be based upon either a hierarchical divisive or Agglomerative Algorithm. Divisive estimation sequentially identifies change points via a bisection Algorithm. The Agglomerative Algorithm estimates change point locations by determining an optimal segmentation. Both approaches are able to detect any type of distributional change within the data. This provides an advantage over many existing change point Algorithms which are only able to detect changes within the marginal distributions.

Joachim M. Buhmann – One of the best experts on this subject based on the ideXlab platform.

  • path based clustering for grouping of smooth curves and texture segmentation
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003
    Co-Authors: Bernd Fischer, Joachim M. Buhmann
    Abstract:

    Perceptual grouping organizes image parts in clusters based on psychophysically plausible similarity measures. We propose a novel grouping method in this paper, which stresses connectedness of image elements via mediating elements rather than favoring high mutual similarity. This grouping principle yields superior clustering results when objects are distributed on low-dimensional extended manifolds in a feature space, and not as local point clouds. In addition to extracting connected structures, objects are singled out as outliers when they are too far away from any cluster structure. The objective function for this perceptual organization principle is optimized by a fast Agglomerative Algorithm. We report on perceptual organization experiments where small edge elements are grouped to smooth curves. The generality of the method is emphasized by results from grouping textured images with texture gradients in an unsupervised fashion.

  • DAGM-Symposium – Data Resampling for Path Based Clustering
    Lecture Notes in Computer Science, 2002
    Co-Authors: Bernd Fischer, Joachim M. Buhmann
    Abstract:

    Path Based Clustering assigns two objects to the same cluster if they are connected by a path with high similarity between adjacent objects on the path. In this paper, we propose a fast Agglomerative Algorithm to minimize the Path Based Clustering cost function. To enhance the reliability of the clustering results a stochastic resampling method is used to generate candidate solutions which are merged to yield empirical assignment probabilities of objects to clusters. The resampling Algorithm measures the reliability of the clustering solution and, based on their stability, determines the number of clusters.

Nicholas A James – One of the best experts on this subject based on the ideXlab platform.

  • ecp: AnRPackage for Nonparametric Multiple Change Point Analysis of Multivariate Data
    Journal of Statistical Software, 2014
    Co-Authors: Nicholas A James, David S Matteson
    Abstract:

    There are many different ways in which change point analysis can be performed, from purely parametric methods to those that are distribution free. The ecp package is designed to perform multiple change point analysis while making as few assumptions as possible. While many other change point methods are applicable only for univariate data, this R package is suitable for both univariate and multivariate observations. Hierarchical estimation can be based upon either a divisive or Agglomerative Algorithm. Divisive estimation sequentially identifies change points via a bisection Algorithm. The Agglomerative Algorithm estimates change point locations by determining an optimal segmentation. Both approaches are able to detect any type of distributional change within the data. This provides an advantage over many existing change point Algorithms which are only able to detect changes within the marginal distributions

  • ecp an r package for nonparametric multiple change point analysis of multivariate data
    arXiv: Computation, 2013
    Co-Authors: Nicholas A James, David S Matteson
    Abstract:

    There are many different ways in which change point analysis can be performed, from purely parametric methods to those that are distribution free. The ecp package is designed to perform multiple change point analysis while making as few assumptions as possible. While many other change point methods are applicable only for univariate data, this R package is suitable for both univariate and multivariate observations. Estimation can be based upon either a hierarchical divisive or Agglomerative Algorithm. Divisive estimation sequentially identifies change points via a bisection Algorithm. The Agglomerative Algorithm estimates change point locations by determining an optimal segmentation. Both approaches are able to detect any type of distributional change within the data. This provides an advantage over many existing change point Algorithms which are only able to detect changes within the marginal distributions.

Bernd Fischer – One of the best experts on this subject based on the ideXlab platform.

  • path based clustering for grouping of smooth curves and texture segmentation
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003
    Co-Authors: Bernd Fischer, Joachim M. Buhmann
    Abstract:

    Perceptual grouping organizes image parts in clusters based on psychophysically plausible similarity measures. We propose a novel grouping method in this paper, which stresses connectedness of image elements via mediating elements rather than favoring high mutual similarity. This grouping principle yields superior clustering results when objects are distributed on low-dimensional extended manifolds in a feature space, and not as local point clouds. In addition to extracting connected structures, objects are singled out as outliers when they are too far away from any cluster structure. The objective function for this perceptual organization principle is optimized by a fast Agglomerative Algorithm. We report on perceptual organization experiments where small edge elements are grouped to smooth curves. The generality of the method is emphasized by results from grouping textured images with texture gradients in an unsupervised fashion.

  • DAGM-Symposium – Data Resampling for Path Based Clustering
    Lecture Notes in Computer Science, 2002
    Co-Authors: Bernd Fischer, Joachim M. Buhmann
    Abstract:

    Path Based Clustering assigns two objects to the same cluster if they are connected by a path with high similarity between adjacent objects on the path. In this paper, we propose a fast Agglomerative Algorithm to minimize the Path Based Clustering cost function. To enhance the reliability of the clustering results a stochastic resampling method is used to generate candidate solutions which are merged to yield empirical assignment probabilities of objects to clusters. The resampling Algorithm measures the reliability of the clustering solution and, based on their stability, determines the number of clusters.

Jian Liu – One of the best experts on this subject based on the ideXlab platform.

  • ICSI (2) – Community detection in sample networks generated from Gaussian mixture model
    Lecture Notes in Computer Science, 2011
    Co-Authors: Ling Zhao, Tingzhan Liu, Jian Liu
    Abstract:

    Detecting communities in complex networks is of great importance in sociology, biology and computer science, disciplines where systems are often represented as networks. In this paper, we use the coarse-grained-diffusion-distance based Agglomerative Algorithm to uncover the community structure exhibited by sample networks generated from Gaussian mixture model, in which the connectivity of the network is induced by a metric. The present Algorithm can identify the community structure in a high degree of efficiency and accuracy. An appropriate number of communities can be automatically determined without any prior knowledge about the community structure. The computational results on three artificial networks confirm the capability of the Algorithm.