paper

An analysis on the use of pre-processing methods in evolutionary fuzzy systems for subgroup discovery

Subgroup discovery is a descriptive data mining technique which aims at obtaining interesting rules through supervised learning. In general, there are no works analysing the consequences of the presence of missing values in data in this task, although improper handling of this type of data in the analysis may introduce bias and can result in misleading conclusions being drawn from a research study.

The Influence of Noise on the Evolutionary Fuzzy Systems for Subgroup Discovery

Abstract

External factors such as the presence of noise in data can affect the data mining process. This is a common problem that produces several negative consequences which involves errors in the data collection, preparation and, above all, in the results obtained by the data mining techniques employed. The capabilities of the models built under such circumstances will depend heavily on the quality of the training data.

Difficult Classes

This website contains additional material to the project related to "Difficult Classes" presented by Alberto Fernández Hilario.

A Preliminary Study on Missing Data Imputation in Evolutionary Fuzzy Systems of Subgroup Discovery

In real-life data, a loss of information is frequent in data mining due to the presence of missing values in the attributes. Missing values can occur due to problems in the manual data entry procedures, equipment errors or incorrect measurements. The presence of missing values in attributes conditions the results obtained by any knowledge extraction approach.