We survey agglomerative hierarchical clustering algorithms and discuss efficient
implementations that are available in R and other software environments. We look at …

We survey agglomerative hierarchical clustering algorithms and discuss efficient
implementations that are available in R and other software environments. We look at …

The paper investigates the acceleration of t-SNE—an embedding technique that is
commonly used for the visualization of high-dimensional data in scatter plots—using two …

METHOD The algorithm requires as input a matrix of M points in N dimensions and a matrix
of K initial cluster centres in N dimensions. The number of points in cluster L is denoted by …

We survey agglomerative hierarchical clustering algorithms and discuss efficient
implementations that are available in R and other software environments. We look at …

An overview is given of prototype‐based models in machine learning. In this framework,
observations, ie, data, are stored in terms of typical representatives. Together with a suitable …

Biological structures can be seen as collections of special devices coordinated by a matrix of
organization. Devices are dif? cult to evolve and are meticulously conserved through the …

The Gaussian process latent variable model (GP-LVM) is a powerful approach for
probabilistic modelling of high dimensional data through dimensional reduction. In this …

A topographic map is a two-dimensional, nonlinear approximation of a potentially high-
dimensional data manifold, which makes it an appealing instrument for visualizing and …

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