While the notion of knowledge is important in many academic disciplines such as philosophy, psychology, economics, and artificial intelligence, the storage and retrieval of data is the main concern of information science. In modern experimental science, knowledge is usually acquired by observing such data, and the cause-effect or association relationships between attributes of objects are often observable in the data.
However, when the amount of data is large, it is difficult to analyze and extract information or knowledge from it. Data mining is a scientific approach that provides effective tools for extracting knowledge so that, with the aid of computers, the large amount of data stored in databases can be transformed into symbolic knowledge automatically.
Data mining, which is one of the fastest growing fields in computer science, integrates various technologies including database management, statistics, soft computing, and machine learning. We have also seen numerous applications of data mining in medicine, finance, business, information security, and so on. Many data mining techniques, such as association or frequent pattern mining, neural networks, decision trees, inductive logic programming, fuzzy logic, granular computing, and rough sets, have been developed. However, such techniques have been developed, though vigorously, under rather ad hoc and vague concepts. For further development, a close examination of its foundations seems necessary. It is expected that this examination will lead to new directions and novel paradigms.