The KDD process is an iterative process that consists in the selection, cleaning and transformation of data coming not only from databases but also from other heterogeneous sources, such as plain text, data warehouses, images, sound, etc., aimed to apply to them data mining algorithms in order to discover valid, novel, potentially useful, and understandable hidden patterns.
Published in Chapter:
Data Mining and the KDD Process
Ana Funes (Universidad Nacional de San Luis, Argentina) and
Aristides Dasso (Universidad Nacional de San Luis, Argentina)
Copyright: © 2018
|Pages: 15
DOI: 10.4018/978-1-5225-2255-3.ch167
Abstract
Nowadays, there exists an increasing number of applications where analysis and discovery of new patterns have fueled the research and development of new methods, all related to Machine Learning, Knowledge Extraction, Knowledge Discovery in Databases or KDD, and Data Mining. The development of Data Mining and other related disciplines has benefited from the existence of large volumes of data proceeding from the most diverse sources and domains. KDD process and methods of Data Mining allows for the discovery of knowledge in data that is hidden to humans, presenting this knowledge under different ways. In this chapter, an overview of the KDD process with special focus in the phase of Data Mining is given. A discussion on Data Mining tasks and methods, a possible classification of them, the relation of Data Mining to other disciplines, and an overview of future challenges in the field are also given.