A rough sets based characteristic relation approach for dynamic attribute generalization in data mining

Research output: Contribution to journalArticlepeer-review


  • Tianrui Li
  • Da Ruan
  • Geert Wets
  • Jing Song
  • Yang Xu

Institutes & Expert groups

Documents & links


Any attribute set in an information system may be evolving in time when new information arrives. Approximations of a concept by rough set theory need updating for data mining or other related tasks. For incremental updating approximations of a concept, methods using the tolerance relation and similarity relation have been previously studied in literature. The characteristic relation-based rough sets approach provides more informative results than the tolerance-and-similarity relation based approach. In this paper, an attribute generalization and its relation to feature selection and feature extraction are firstly discussed. Then, a new approach for incrementally updating approximations of a concept is presented under the characteristic relation-based rough sets. Finally, the pproach of direct computation of rough set approximations and the proposed approach of dynamic maintenance of rough set approximations are employed for performance comparison. An extensive experimental evaluation on a large soybean database from MLC shows that the proposed approach effectively handles a dynamic attribute generalization in data mining.


Original languageEnglish
Pages (from-to)485-494
JournalKnowledge-Based Systems
Issue number5
Publication statusPublished - Jun 2007


  • Rough sets, Knowledge discovery, Data mining, Incomplete information systems

ID: 314054