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yxhlwh's Projects
angular-parallel-coordinates
Angular directive for creating parallel coordinates charts using D3
correlation-scatter
Correlation matrix with scatter plot using d3.js
cs686-d3
Explore with d3 complicated examples such as parallel coordinates and Scatter plot matrix, etc.
d3
A JavaScript visualization library for HTML and SVG.
evolution-internet-users
Process data for Evolution of Internet Users dataviz
jqplot.scattergraph
Scatter Graph plugin for jqPlot
maxmsp_dissimilarity
A Max/MSP object for collecting dissimilarity data, for analysis using multidimensional scaling
mds
Perform multi-dimension scaling
mdscale
A free and open-source Multidimensional Scaling (MDS) library for Java.
parallel-coordinates
A d3-based parallel coordinates plot in canvas.
parallel-coordinates-chart
Parallel coordinates chart for browsers using D3
parcoords
R htmlwidget for parallel-coordinates d3 chart
perfdot
This is a graph built using D3.js to make scatter plot which can be used to visualize categorically distributed data.
programming-languages-influence
Code to retrieve data for the programming languages influence visualizations from Freebase
scatter-matrix
Explore data matrix using scatterplot matrix
scatter-plot
graph
scatter-vector
d3 scatterplot matrix, one row at a time
scattermatrixd3
R htmlwidget wrapper around the D3 scatterplot matrix
som
SOM - Self organizing Map is a Swing application that implements the Self organizing map algorithm. Self-organizing map (SOM) is a type of artificial neural network that is trained using unsupervised learning to produce low-dimensional representation of the training samples while preserving the topological properties of the input space. Self-Organizing Map showing US Congress voting patterns visualized in Synapse Self-Organizing Map showing US Congress voting patterns visualized in Synapse This makes SOM useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map. Like most artificial neural networks, SOMs operate in two modes: training and mapping. Training builds the map using input examples. It is a competitive process, also called vector quantization. Mapping automatically classifies a new input vector.
students-filters
WEKA plugin for FastICA and multidimensional scaling filters
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