Performance Evaluation of Feature Selection Methods on Large Dimensional Databases

Abstract

Data mining retrieves knowledge information from larger amounts of data. Clustering is an assemble of similar objects in to one class and dissimilar objects in to another class. When designing clustering ensemble on large dimensional data space, both time and space requirements for processing may be overinflated. This tends to impose feature selection methods to remove redundant features and handle the noise data. There are filter, wrapper and hybrid methods in feature selection. This paper shows a tour on types of feature selection techniques and numbers of experiments are conducted to compare feature selection techniques using different datasets with R tool, which gives better technique for clustering ensemble design.