ABSTRACT
Data preprocessing converts raw data and signals into data representation suitable for application through a sequence of operations. The objectives of data preprocessing include size reduction of the input space, smoother relationships, data normalization, noise reduction, and feature extraction. This chapter discusses several data preprocessing algorithms, such as data values averaging, input space reduction, and data normalization. It provides computer programs for data preprocessing. A pattern is an entity to represent an abstract concept or a physical object. It may contain several attributes to characterize an object. Reducing the number of input variables or the size of the input space are a common goal of the preprocessing. For many practical problems, the units used to measure each of the input variables can skew the data and make the range of values along some axes much larger than others. The input space reduction can be achieved by removing highly correlated input variables.
