… The cover-tree data structure provides an approximate estimate in O(d log N) query time. …
(Iris data set) We perform density estimation on this 4 dimensional data set and compute …

… data analysis through nonparametric density estimation for a … revised presentation, Multivariate
Density Estimation: Theory, … and supporting theory of density estimation. Including new …

… data analytics. This paper reviews a collection of selected nonparametric density estimation
algorithms for high-dimensional data, … domain of nonparametric density estimation, such as …

… Information visualization has addressed the visualization of various kinds of abstract data
structures, like networks and text corpuses. Our main emphasis is on the visualization of …

… in the data sample. Unlike parametric or finite-mixture approaches to density estimation where
… required in estimation, Parzen1 density estimates employ the full data sample in defining …

… and compare several data structures and algorithms for evaluating the kernel density
estimate. … Finally, to be able to make a clear distinction between the different data structures for …

… In this paper each of these models will be either a finite mixture with a fixed number of
component densities or a kernel density estimate with a fixed kernel and a single fixed global …

… our numerical estimates and to provide the appropriate bias for the structure selection task.
… We also tested our algorithm as a density estimator. For these tests, we used a domain with …

… ods to estimate the density of the data. In this paper we propose a method for density estimation
that … probability density and a method to visualize high dimensional density distributions. …

… most basic and intuitive method of density estimation: the histogram. Then, in Section 3 we
introduce kernel density estimation and the properties of estimators of this type, followed by …