BibTeX
@MISC{Last99automatedperceptions,
author = {Mark Last and Abraham Kandel},
title = {Automated Perceptions in Data Mining},
year = {1999}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
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. Keywords: Data mining, Fuzzy set theory, data visualization, data perception, rule extraction. 1. Introduction Fayyad et al. [3] have defined the process of knowledge discovery in databases (K...
Keyphrases
data mining automated perception data set fuzzy membership function multiple perception efficient data mining approach high dimensionality automated task visual data analysis data perception noisy data dependent variable human perception poor scalability several real-world data set frequency distribution introduction fayyad rule extraction complex pattern different user data visualization main limitation knowledge discovery human eye fuzzy set theory
