Impact of Using Pnepmoessing in Data Mining and Knowledge Discovery Process

Abstract

Abstract Data mining works to extract information known in advance from the enomous quantities of data which can lead to knowledge. It provides information that helps to make good decisions. The effectiveness of data mining in access to knowledge to achieve the goal of which is the discovery of the hidden facts contained in databases and through the use of multiple technologies. Unfortunately, real-world databases are highly influenced by negative factors such the presence of noise, inconsistent and superfluous data and huge sizes in dimensions, examples and features. Thus, low-quality data will lead to low-quality Data Mining performance. _ Data pre-processing is a first step of Data Mining in Knowledge discovery process (KDD) 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. This paper study the huge impact of preprocessing in data mining by prepare the data (clean it, transform it, integrate it) to produce a good data that leads to high quality data mining performance. Keywords: Preprocessing, Data Mining, Knowledge Discovery, Data Preparation.