Knowledge Mining: A Proposed New Direction
Ryszard S. Michalski · 2003
In the last several years, the field of data mining has been rapidly expanding, and attracting many new researchers and users. The underlying reason for such a rapid growth is a great need for systems that can automatically derive useful knowledge from vast volumes of computer data being accumulated worldwide. The field of data mining offers a promise for addressing this need. The major trust of research has been to develop a repertoire of tools for discovering both strong and useful patterns in large databases. The function performed by such tools can be succinctly characterized as a mapping: DATA → PATTERNS (1) An underlying assumption is that the patterns are created solely from the data, and thus are expressed in terms of attributes and relations appearing in the data. Determining such patterns can be a problem of significant computational complexity, but of a relatively low conceptual complexity, and many efficient algorithms have been developed for this purpose (e.g., Breiman et al., 1984; Quinlan, 1993, Agrawal et al., 1996; Witten, Moffatt and Bell, 1999). This approach to the problem of deriving useful knowledge from databases has, however, some fundamental limitations, and new research should address