A Knowledge Innovation Algorithm Based on Granularity

yan taishan


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. Knowledge innovation is an important step of knowledge evolution course. Based on knowledge granularity, a knowledge innovation method was 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. There is only one computation namely the measure consistency of attribute in the algorithm, so the numerical calculation work is little, the time complexity is low, and the algorithm is feasible absolutely. Experiments were taken on the imperfect knowledge base space of day weather classification by this algorithm, its working course was explained. The successful results show that this algorithm is correct and valid.

Full Text:


DOI: https://doi.org/10.5539/cis.v3n1p152

Copyright (c)

Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)  Email: cis@ccsenet.org

Copyright © Canadian Center of Science and Education

To make sure that you can receive messages from us, please add the 'ccsenet.org' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.