Automated perceptions in data mining

Visualization is known to be one of the most efficient data mining approaches. The human eye can capture complex patterns and relationships, along with detecting the outlying (exceptional) cases in a data set. The main limitation of the visual data analysis is its poor scalability: it is hardly applicable to data sets of high dimensionality. We use the concepts of Fuzzy Set Theory to automate the process of human perception. The automated tasks include comparison of frequency distributions, evaluating reliability of dependent variables, and detecting outliers in noisy data. Multiple perceptions (related to different users) can be represented by adjusting the parameters of the fuzzy membership functions. The applicability of automated perceptions is demonstrated on several real-world data sets.