  Visualization of extremely large datasets in static or dynamic form is a huge
challenge because most traditional methods cannot deal with big data problems.
A new visualization method for big data is proposed based on Projection
Pursuit, Guided Tour and Data Nuggets methods, that will help display
interesting hidden structures such as clusters, outliers, and other nonlinear
structures in big data. The Guided Tour is a dynamic graphical tool for
high-dimensional data combining Projection Pursuit and Grand Tour methods. It
displays a dynamic sequence of low-dimensional projections obtained by using
Projection Pursuit (PP) index functions to navigate the data space. Different
PP indices have been developed to detect interesting structures of multivariate
data but there are computational problems for big data using the original
guided tour with these indices. A new PP index is developed to be computable
for big data, with the help of a data compression method called Data Nuggets
that reduces large datasets while maintaining the original data structure.
Simulation studies are conducted and a real large dataset is used to illustrate
the proposed methodology. Static and dynamic graphical tools for big data can
be developed based on the proposed PP index to detect nonlinear structures.
