Visualizing  and making sense of multivariate data geometrically in the Euclidean space  is very challenging to say the least when more than three variables are in question.

This week, the introduction of Chernoff Faces as a tool for graphing multivariate data made dealing with so many variables bearable, and making sense of the data as a whole more intuitive. Different data dimensions map onto different facial features. The example (taken from “FlowingData”, http://flowingdata.com/2010/08/31/how-to-visualize-data-with-cartoonish-faces/) below will show how Chernoff Faces are constructed and how easier it makes data interpretation.

Parallel Coordinates was another graphical representation of multivariate data that was introduced. In this method, each variable is assigned its own  vertical axis and each axis is parallel to the other. A horizontal line between any axes implies positive correlation while an intersection  implies negative correlation. It seems to be a good tool for measuring  the association between variables.

Star plotting was briefly mentioned at the meeting, and the Iris data set was used to calculate the chances of a reading ( that isn’t isolated nor distinctively associated with any near by clusters of readings) being  apart of any cluster surrounding it

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