A Hybridized Rough-PCA Approach of Attribute Reduction for High Dimensional Data Set

Owing to the development of novel techniques for generating and collecting data, the rate of growth of scientific databases has become tremendous, which creates both a need and an opportunity to extract implicit knowledge to analyze these datasets. Analysis of such large expression data gives rise to a number of new computational challenges not only due to the increase in no. of data objects but also due to the increase in no of attributes. Hence to improve the efficiency and accuracy of mining task on high dimensional data, the data must be preprocessed by an efficient dimensionality reduction method.