Latest. As I mentioned in my previous post, our collaboration with the Sabeti Lab is aimed at creating new visual exploration tools to help researchers, doctors, and clinicians discover patterns and associations in large health and epidemiological datasets. These tools will be the first step in a hypothesis-generation process, combining intuition from expert users with visualization techniques and automated algorithms, allowing users to quickly test hypothesis that are “suggested” by the data itself. Researchers and doctors have a deep familiarity with their data and often can tell immediately when a new pattern is potentially interesting or simply the result of noise.
Visualization techniques will help articulate their knowledge to a wider audience. This time around I will describe a quantitative measure of statistical independence called mutual information, which is used to rank associations in the data. -log 1/1000 = log 1000 = 6.9 -log 1/1000000 = log 1000000 = 13.81 -log 1/2 = log 2 = 0.69. Well-formed data. Information aesthetics - Data Visualization & Information Design. Data Visualization Blog The Excel Charts Blog. Chart Porn.
Datavisualization.ch. Neoformix - Discovering and Illustrating Patterns in Data.