Search Results for "clustering"

The goal of DynaQ is to develop an inquiry system to explore the personal information space, supporting you with the searching paradigm 'orienteering'. DynaQ is a (desktop)search engine with enhanced functionality for file, email and blog search. Look at our GitLab homepage for sourcecode and documentation: http://dynaq.opendfki.de

Spectral Python (SPy) is a python package for reading, viewing, manipulating, and classifying hyperspectral image (HSI) data. SPy includes functions for clustering, dimensionality reduction, supervised classification, and more.

...It allows arranging multiple heatmaps, adding annotations, combining heatmaps, customizing colors, layouts, and integrating other plots. Often used in genomics/bioinformatics to show expression, methylation, etc., with sidebars, annotations, clustering, etc. Highly customizable layout: combining different heatmaps, arranging and splitting, dealing with multiple heatmap merges, combining with other plots etc. Integration with Shiny / interactive heatmaps via companion packages (InteractiveComplexHeatmap) to allow interactivity, etc.

ImagingAnalysis is a Mathematica package that performs grid-based analysis of time-lapse imaging data saved in a sequence of TIFF files. This package requires Mathematica 7.0. Revised on 14 May 2017: Bugs are fixed and incompatibility issues are resolved. The current version runs on Mathematica 11.

Open source animation of kmeans clustering in X Window System using the C++ libplotter library. Supports Linux, Mac, and BSD. Includes common initialization methods such as Forgy, Macqueen, random, and angular. Sample videos are available through the Files Tab above. The SVN repo is accessible thorugh the Code Tab above. Requires a C++ compiler, libplot-dev, and libncurses5-dev Mac alternative to libplot-dev: macports plotutils +x11

PBS Cluster Viz is a project to display information useful to admins and users about a computing cluster managed by a PBS-compatible resource manager. Information includes load and job distribution. Interactive as well as static output is available.

Flow Investigation using N-Dimensions (FIND) is a program designed for analysis and visualization of Flow Cytometry data. FIND focuses specifically on automated population discovery (clustering) methods. The project targets both users and developers.

SLEDRIDE: Simplified Learning about Expression Data Running in a Desktop Environment. To provide a general workbench for pipe-lining microarray gene expression data from supervised learning results into unsupervised learning methods.

Construct and management virtual clusters which the nodes of the clusters running on the virtual machine rather than native machine. Fast install and switch virtual clusters. Efficiently storage vm images. support variant VMMs.

ClutrFree allows the visualization and interpretation of gene expression data clusters for multiple clustering experiments. Data is integrated with ontological annotations (GO), and enrichement of annotations (hypergeometric distribution) is computed.

The Parallel Compositing library enables developers to write scalable 3D graphics applications. The library does sort-last compositing, and works on both clusters and SMP machines. The API aims to be the MPI of parallel compositing.

This is an opensource project for the calculation of the mathematical constant Pi. If you are a mathematician or programer please contribute ideas or code for this project. Non professionals are also welcome to contribute.

growler is a C++-based distributed object and event architecture. Its primary application has been in support of interactive, distributed visualization, "concurrent visualization", and computational steering.

The Databionics ESOM Tools offer many data mining tasks using Emergent Self-Organizing Maps. Visualization, clustering, and classification of high-dimensional data using databionics principles can be performed interactively or automatically.

Nodemon is a visualization tool for monitoring system resource utilization. It was developed for monitoring the Columbia supercomputer, a 10,240-processor Linux system at NASA Ames Research Center. It can monitor resources on any Linux system or cluster

A high performance visualization tool for stars and particles able to run on Beowulfs. Rendering step is implemented separately and optimized for SSE, compatible with OpenGL. Navigation mechanism with quaternions. Ten color maps, sobel contour detect

UPDATE: Chromium is no longer updated or maintained. The project is frozen. Chromium is a flexible framework for scalable real-time rendering on clusters of workstations, derived from the Stanford WireGL project code base.

clusterviz allows to cluster three-dimensional data. The clustering process is visualized using OpenGL. As clustering algorithms the family of k-means algorithms is implemented, including mixture models.

Prototype for a framework and user interface for combining various structured search and document clustering techniques.

MicroArray Genome Imaging and Clustering Tool (MAGIC tool) is a platform-independant java program for analyzing MicroArray data (.tiff scans & .txt godlists) via graphs and clustering operations (including QT-clustering). http://www.bio.davidson.edu/magic

JCGrid is an easy-to-use tools for real-world grid-computing. It requires only to install Java Runtime and you can easy split your cpu-intensive task among several Workstations/PC/Mac/etc. It includes support for POVRay and MandelGrid as usage examples.

a 3d replacement for mosix's boring text based mon program. Interaces with gomd (www.nonngu.org/gomd) so you dont have to run this big openGL app on your cluster, just a handy server.

TM4 is a suite of applications for managing and analyzing microarray data. TM4 provides data storage and tracking, image analysis, normalization, data filtering, clustering and statistical analysis capabilities. Includes MADAM, Spotfinder, MIDAS, and MeV.

This is educational software whose purpose it is to clarify the workings of different clustering algorithms by visualising the clustering process in 2D space. This make many possible problems and advantages very clear.

Pattern recognition software package. It includes several classification and clustering algorithms. It can read data from a set of images, an ASCII file or a JDBC connection. A small TCP data server with its corresponding JDBC driver is included.