Data Preparation in Neural Network Data Analysis

Abstract

Preparing data is an important and critical step in neural network data analysis and it has an immense impact on the success of a wide variety of complex data analysis, such as data mining and knowledge discovery (Hu, 2003). The main reason is that the quality of the input data into neural network models may strongly influence the results of the data analysis (Sattler and Schallehn, 2001). As Lou (1993) stated, the effect on the neural network’s performance can be significant if important input data are missing or distorted. In general, properly prepared data are easy to handle, which makes the data analysis task simple. On the other hand, improperly prepared data may make data analysis difficult, if not impossible. Furthermore, data from different sources and growing amounts of data produced by modern data acquisition techniques have made data preparation a time-consuming task. It has been claimed that 50–70 percent of the time and effort in data analysis projects is required for data preparation (Sattler and Schallehn, 2001; Pyle, 1999). Therefore, data preparation involves enhancing the data in an attempt to improve the performance of data analysis.