Narrow Results

It is very difficult to discover valid knowledge in the data repositories and is also very difficult to choose suited data mining methods without prior knowledge about data mining or application domain since the amount of raw data becomes large and there are a variety of data mining methods. In this paper, we propose a framework of an Intelligent Knowledge Discovery System (IKDS) to help users select appropriate data mining algorithms and discover knowledge. In addition, a knowledge acquisition methodology, SIMCAP, is also proposed to elicit not only explicit knowledge but also implicit knowledge of the experts. The knowledge in IKDS can be represented and stored by XML. The prototypes of SIMCAP and IKDS have been built up to help users discover knowledge.

In order to overcome the shortcoming of general rough set theory, the elementary concept of tolerance rough set theory is proposed in this paper, and is used to build objects’ tolerance relations that can correctly classify objects in system. We use genetic algorithms to search for the optimal thresholds, then construct special matrix for attributes and objects. We can get the relations among attributes, objects and relative absorbent set of objects in detail. The method of using tolerance rough sets reduces the qualitative processing and improves the validness of knowledge reduction. Finally, we present examples, illustrating our approach in the paper.