I. Introduction
Data mining refers to the task of analyzing large amount of data with intend of finding hidden patterns and trends that are not immediately apparent from summarized data. Data mining and knowledge extraction from raw data is becoming more and more important and useful as the amount and complexity of data is rapidly increasing. Data mining commonly involves four classes of tasks: Classification - arranges the data into predefined groups, Clustering - is similar to classification but the groups are not predefined, so the algorithm will try to group similar items together, Regression - attempts to find a function which models the data with the least error and Association rule learning - searches for relationships between variables [16]. Data has become highly available now-a-days and consists of complex structures and high dimensions. In order to achieve accuracy in classification of such data, we require identifying and removing irrelevant and insignificant dimensions. The process of reducing dimensions is referred as Dimensionality Reduction. It is a crucial pre-processing step in Data Mining to improve computational efficiency and accuracy. Dimensionality reduction provides benefits such as improved dataset classification accuracy, increased computational efficiency and better visualization of dimensions.