Search Results for "clustering"
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Showing 46 open source projects for "clustering"
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Clustering.jl
A Julia package for data clustering
Methods for data clustering and evaluation of clustering quality. -
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The ARCHModels Package for Julia
A Julia package for estimating ARMA-GARCH models
ARCH (Autoregressive Conditional Heteroskedasticity) models are a class of models designed to capture a feature of financial returns data known as volatility clustering, i.e., the fact that large (in absolute value) returns tend to cluster together, such as during periods of financial turmoil, which then alternate with relatively calmer periods. This package provides efficient routines for simulating, estimating, and testing a variety of GARCH models. ARCH (Autoregressive Conditional Heteroskedasticity) models are a class of models designed to capture a feature of financial returns data known as volatility clustering, i.e., the fact that large (in absolute value) returns tend to cluster together, such as during periods of financial turmoil, which then alternate with relatively calmer periods. -
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Orange Data Mining
Orange: Interactive data analysis
...Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections. Interactive data exploration for rapid qualitative analysis with clean visualizations. Graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy. ... -
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HyperTools
A Python toolbox for gaining geometric insights
...Functions for plotting high-dimensional datasets in 2/3D. Static and animated plots. Simple API for customizing plot styles. Set of powerful data manipulation tools including hyperalignment, k-means clustering, normalizing and more. Support for lists of Numpy arrays, Pandas dataframes, text or (mixed) lists. Applying topic models and other text vectorization methods to text data. HyperTools is designed to facilitate dimensionality reduction-based visual explorations of high-dimensional data. The basic pipeline is to feed in a high-dimensional dataset (or a series of high-dimensional datasets) and, in a single function call, reduce the dimensionality of the dataset(s) and create a plot. -
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Emerge
Browser-based interactive codebase and dependency visualization tool
...Emerge currently has parsing support for the following languages: C, C++, Groovy, Java, JavaScript, TypeScript, Kotlin, ObjC, Ruby, Swift, Python, and Go. The structure, coloring, and clustering is calculated and based on the idea of combining a force-directed graph simulation and Louvain modularity. emerge is mainly written in Python 3 and is tested on macOS, Linux, and modern web browsers (i.e., the latest Safari, Chrome, Firefox, and Edge). -
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awesome-single-cell
Community-curated list of software packages and data resources
...The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization, dimension reduction, clustering, differential expression and a wide-range of plotting. An analytical framework for big-scale single cell data. Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction. -
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Social Network Visualizer
Social Network Analysis and Visualization software
Visit our new site: http://socnetv.org Social Network Visualizer (SocNetV) is a social network analysis and visualization application. You can draw a social network (graph/digraph) or load an existing one (GraphML, UCINET, Pajek, etc), compute cohesion, centrality, community and structural equivalence metrics and apply various layout algorithms based on actor centrality or prestige scores (i.e. Eigenvector, Betweenness) or on dynamic models (i.e. Kamada-Kawai spring-embedder) -
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DynaQ
Innovative text document search. http://dynaq.opendfki.de for details.
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 -
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NOVA
Analysis and visualization of complexome profiling data.
NOVA is a program designed to analysis complexome profiling data (Heide et al., 2012). A graphical user interface (GUI) provides various visualization tools, such as heat maps and 2D plots. Several hierarchical clustering algorithms (e.g., single linkage, average linkage, Wards linkage), different distance measures (e.g., Euclidean distance, Manhattan distance, Pearson distance), and various normalization techniques are implemented. Many additional functions like zooming, searching for proteins, image export, and automatic file format recognition support intuitive handling for biologists. ... -
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Spectral Python
A python module for hyperspectral image processing
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. -
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ComplexHeatmap
Make Complex Heatmaps
...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. -
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VividSTORM
Correlated confocal and SMLM data visualization and analysis
VividSTORM is a free and open-source standalone software with graphical user interface, for the correlated visualization and analysis of superresolution single molecule localization microscopy (SMLM) molecule lists and conventional pixel intensity-based images. The localization points (LPs) within this ROI can be analyzed using the selected built-in functions. NOTE: If you encounter issues not addressed by the user guide, please contact by message on this site or via e-mail for... -
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irayMol
Molecular visualization
Analyses and Interactive visualizations of the structures, functions and actions of biomolecules including molecular surface computation, and protein-ligand interface and protein-ligand docking where the ligand could a small compound, a nucleic acid, membrane and other proteins, written in C++/Qt/openGL/GLSL with more than 125,000 lines of codes. In addition to Qt, the only external library needed is GSL everything else is coded from the scratch. -
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ImagingAnalysis
Direct tissue-level image quantification package for Mathematica
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. -
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KMeansAniX
Animation of kmeans clustering using X Window System
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 -
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Visualization and clustering analysis of X-Ray diffraction data from combinatorial thin film libraries.
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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.
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WaveSorter
A powerful, versatile tool for offilne spike analysis and sorting
...Within the waveform space, cursor-based controls let the user select subregions of the waveform space or individual waveforms to view. The user may cluster waveforms manually or via one of several popular clustering programs. The classification along with waveform properties (width, etc.) can be saved to disk in simple text files. WaveSorter is written in C++, utilizes the GNU Scientific Library for all computation, and is highly parallelized; on modern hardware it can handle files containing several 100,000s of waveforms per channel with almost no noticeable loss in GUI fluidity and <1sec lags for files with >1,000,000 waveforms per channel. ... -
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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.
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The final build of this software now is distributed in R, embedded in "RedeR': an R/Bioconductor package for hierarchical and nested network analysis... more about RedeR: http://bioconductor.org/packages/2.9/bioc/html/RedeR.html
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Fish is a visual cluster monitoring program that uses Ganglia to portray each node in a cluster as a separate fish with characteristics that describe the state of the node.
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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.
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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.
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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.
