ON ROBUST INFORMATION EXTRACTION FROM HIGH- DIMENSIONAL DATA

High-dimensional data are often encountered in management applications with the aim to perform a decision making, which can be described as selecting an activity or series of activities among several alternatives (Martinez et al., 2011). Data mining methods for information extraction from high-dimensional data represent an important tool allowing to find answers to given questions concerning a fixed database or to generate hypotheses from a random sample. High-dimensional data are usually understood to have a form of a data set with a large number of observations and/or a large number of variables. Statisticians usually consider a situation with a small number o