Discernible visualization of high dimensional data using label information
Visualization methods could significantly improve the outcome of automated knowledge discovery systems by involving human judgment. Star coordinate is a visualization technique that maps k-dimensional data onto a circle using a set of axes sharing the same origin at the center of the circle. We propose a novel method toward automatic axes adjustment for high dimensional data in Star Coordinate visualization method. This method finds the best 2-dimensional view point (discernible visualization) that minimizes intra-cluster distances while keeping the inter-cluster distances as large as possible by using label information. The label information could be provided by the user or could be the result of performing a conventional clustering method over the input data.
visualization star coordinate high dimensionality reduction fishers discriminant form