Search results
Results from the WOW.Com Content Network
The quality of DBSCAN depends on the distance measure used in the function regionQuery(P,ε). The most common distance metric used is Euclidean distance. Especially for high-dimensional data, this metric can be rendered almost useless due to the so-called "Curse of dimensionality", making it difficult to find an appropriate value for ε. This ...
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 ...
It is a nonlinear dimensionality reduction technique for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions. Specifically, it models each high-dimensional object by a two- or three-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are ...
Kernel density estimate with diagonal bandwidth for synthetic normal mixture data. We consider estimating the density of the Gaussian mixture (4π) −1 exp(− 1 ⁄ 2 (x 1 2 + x 2 2)) + (4π) −1 exp(− 1 ⁄ 2 ((x 1 - 3.5) 2 + x 2 2)), from 500 randomly generated points. We employ the Matlab routine for 2-dimensional data.
English: Cluster analysis with DBSCAN on a density-based data set. Algorithm and data set are a perfect match for each other. Algorithm and data set are a perfect match for each other. The visualization was generated using ELKI .
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters).
The R package "dbscan" includes a C++ implementation of OPTICS (with both traditional dbscan-like and ξ cluster extraction) using a k-d tree for index acceleration for Euclidean distance only. Python implementations of OPTICS are available in the PyClustering library and in scikit-learn. HDBSCAN* is available in the hdbscan library.
Thus, the study of visualization of high-dimensional spaces is of central importance to TDA, although it does not necessarily involve the use of persistent homology. However, recent attempts have been made to use persistent homology in data visualization. [28] Carlsson et al. have proposed a general method called MAPPER. [29]