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Portfolio Fabian Dill | Information & Visualization
KNIME is a modular data mining platform with a focus on interactive exploration. I substantially improved this feature with a (sub-)framework realizing general visualization features. Based on this framework I implemented seven statistical views: scatter plot, scatter matrix, line plot, box plot, parallel coordinates, a dendrogram, and 2D rule plot. Almost all KNIME views are now based on this framework.
Which version should you use? Piccolo2D In Comparison will explain the differences between Piccolo2D.Java and Piccolo2D.NET as well as how Piccolo2D relates to other Graphics Toolkits.
Learning Center
Read the manual (.pdf, 1meg) Here you can find a tutorial to help you navigate your way around the ggobi interface, reading in data, and information about each of the parts of the interface. See ggobi in use as teaching tool for investigation classification, contours of bi- and tri-variate normal distributions, factor analysis, self organising maps and multivariate confidence regions. Linked brushing between plots can be more complex, using categorical variables to define the connection, or linking different plot elements.
Documentation. GGobi data visualization system.
Principal Manifolds for Data Visualization and Dimension Reduction
In 1901 Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial PCA deciphers genome.Data Visualization: Modern Approaches - Smashing Magazine
Data presentation can be beautiful, elegant and descriptive. There is a variety of conventional ways to visualize data – tables, histograms, pie charts and bar graphs are being used every day, in every project and on every possible occasion. However, to convey a message to your readers effectively, sometimes you need more than just a simple pie chart of your results. In fact, there are much better, profound, creative and absolutely fascinating ways to visualize data . Many of them might become ubiquitous in the next few years.
Data visualization is the study of the visual representation of data , meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information". [ 1 ] According to Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn’t mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information". [ 2 ] Indeed, Fernanda Viegas and Martin M.