A Knowledge Innovation Algorithm Based on Granularity

The structure of human knowledge is regarded as granule state by rough sets theory. Granularity is used to denote this structure of knowledge. Knowledge itself evolves ceaselessly as creatures. The mechanism of knowledge evolution includes the productive mechanism for new knowledge and the natural choice mechanism for the selection of the superior and the elimination of the inferior. Knowledge innovation is an important step of knowledge evolution course. Based on knowledge granularity, a knowledge innovation method is proposed in this paper. The main idea of this method is to constitute the partition granularity of knowledge base space ceaselessly depend on the measure consistency of attribute, till the sort of every granules in the granularity is only one. In the algorithm, the only one computation work is to measure the consistency of attributes in knowledge base space. So the numerical calculation is little and the time complexity is low. Experiments were taken on the imperfect knowledge base space of day weather classification by this algorithm. The working course of the algorithm was explained in the example. The successful results show that this algorithm is valid and feasible