A Systematic Approach on Data Pre-processing In Data Mining

Abstract

Data pre-processing is an important and critical step in the data mining process and it has a huge impact on the success of a data mining Soil classification. Data pre-processing is a first step of the Knowledge discovery in databases (KDD) process that reduces the complexity of the data and offers better analysis and ANN training. Based on the collected data from the field as well soil testing laboratory, data analysis is performed more accurately and efficiently. Data pre-processing is challenging and tedious task as it involves extensive manual effort and time in developing the data operation scripts. There are a number of different tools and methods used for pre-processing, including: sampling, which selects a representative subset from a large population of data; transformation, which manipulates raw data to produce a single input; denoising, which removes noise from data; normalization, which organizes data for more efficient access; and feature extraction, which pulls out specified data that is significant in some particular context. Pre-processing technique for soil data sets are also useful for classification in data mining.