Exploring High Dimensional Data with the Rank-by-Feature Framework

Goal: Find Features in Multi-D Data

•Finding correlations, clusters, outliers, gaps, à Cognitive difficulties in >3D

•Therefore utilize low-dimensional projections

•Perceptual efficiency in 1D and 2D

•Use Rank-by-Feature Framework to guide discovery


	The goal we want to achieve with the new idea is to find interesting features in multidimensional data.

	Finding features like correlations, clusters, outliers, gaps is difficult in multidimensional data,
	because of the cognitive difficulties in understanding more than 3 dimensions.

	Therefore we need to utilize low-dimensional projections since the human visual system is very effective in 1D and 2D.

	So, the rank-by-feature framework use 1D and 2D projections to guide discovery process.