Dimesionality Reduction using Association Rule Mining

Abstract

When data objects that are the subject of analysis using machine learning techniques are described by a large number of feature (i.e. the data is high dimension) it is often beneficial to reduce the dimension of the data. dimensionality reduction (DR) can be beneficial not only reasons of computational efficiency but also because it can improve the accuracy of the analysis. Now we have tried to introduce a novel transform to achieve dimensionality reduction. This paper summarizes survey on feature selection and extraction from high-dimensionality data sets using genetic algorithm. The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, to obtain the accuracy and saves the computation time and simplifies the result. We are trying to develop GA-based approach utilizing a feedback linkage between feature evaluation and association rule. That is we carry out feature selection simultaneously with association rule mining, through "genetic learning and evolution.