Visual Analytics for Exploration of a High-Dimensional Structure

This report is about a stage of the knowledge discovery in databases (KDD) process used to find possible patterns in high-dimensional data (HDD): data distributed in the form of a geometrical locus (or object) in HDD space or data close to some manifold. The emphasis is on data mining for exploratory data analytics of the HDD and dimensionality reduction by feature selection/extraction, which is necessary for a two- or three-dimensional representation of the HDD for exploratory visual analytics. Such a description allows us to navigate and interact with the data. The original document contains color images.