International Journal of Computer Applications
Dimension reduction is the process of keeping only those dimensions in a dataset which are important from the point of view of problem at hand and discarding of the others. This paper helps to design easily computable algorithms and to increase the performance of classifiers. It has gained importance as a preprocessing step in knowledge discovery and data mining especially in the fields of pattern matching, machine learning, bioinformatics and genetics which involve datasets having large number of dimensions. There are two basic strategies used for reduction of the dimensions; feature selection and feature extraction.