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Proper Citation: Divvy (RRID:SCR_006336)
Description: Software application for performing unsupervised machine learning and visualization with a focus on the clustering (separating data into groups) and dimensionality reduction (finding low dimensional structure in high dimensional data) subfields of machine learning. For visualization we provide support for both the whole dataset (e.g. a scatter plot) and points (e.g. transforming a particular point into an image). * Endlessly extensible. Every clusterer, reducer, point visualizer and dataset visualizer in Divvy is a plugin. We''ve provided a few big ones (K-means, PCA, scatter plot, &c.) and we''re hoping the community will use our plugin protocol to build many more. Each plugin defines its own UI, so your algorithm can look and behave the way that you want it to without top-down constraints. * Have lots of cores? Divvy is both task and data parallel. No longer will you be waiting for one algorithm to complete before you start another. Start as many as you want and keep using the UI. Only started one? With data parallelism we''ll still push your new MacBook Pro to 800% CPU utilization. * Part of your workflow: Export your clusterings and reductions to .csv and your visualizations to .png. Use your Matlab or R data with our Matlab/R to Divvy export tools available at http://github.com/jmlewis/divvy.
Abbreviations: Divvy
Resource Type: software application, data visualization software, source code, data analysis software, data processing software, software resource
Keywords: data analysis, cluster, machine learning, visual analytics, data visualization, plugin, k-means, principal component analysis, scatter plot
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