Data Mining and Knowledge Discovery

In Chapters 2 and 3, the knowledge-based system and Multi-Agent system were illustrated. These are significant methods and theories of Manufacturing Intelligence (MI). Data Mining (DM) and Knowledge Discovery (KD) are at the foundation of MI. Humans are immersed in data, but are thirsty for knowledge. With the wider application of database technology, a dilemma has arisen whereby people are ‘rich in data, poor in knowledge’. The explosion of knowledge and information has brought great benefit to mankind, but has also carried with it certain drawbacks, since it has resulted in knowledge and information ‘pollution. Facing a vast but polluted ocean of data, a technical means to discard the bad and retain the good was sought. Data Mining and Knowledge Discovery (DMKD) was therefore proposed against the background of rapidly expanding data and databases. It is also the result of the development and fusion of database technology, Artificial Intelligence (AI), statistical techniques and visualization technology (Fayyad U., 1998). DMKD has become a research focus and cutting-edge technology in the field of computer information processing (Jef Woksem, 2001). The development background, conception, working process, classification and general application of DM and KD are firstly introduced in this chapter. Secondly, basic functions and assignment such as prediction, description, data clustering, data classification, conception description and visualization processing are discussed. Then the methods and tools for DM are presented, such as the association rule, decision tree, genetic algorithm, rough set and support vector machine. Finally, the application of DMKD in intelligent manufacturing is summarized.