Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Technical Note: Bias and the Quantification of Stability
Machine Learning - Special issue on bias evaluation and selection
Data mining: concepts and techniques
Data mining: concepts and techniques
Investigation on AQ11, ID3 and the principle of discernibility matrix
Journal of Computer Science and Technology
Reduction algorithms based on discernibility matrix: the ordered attributes method
Journal of Computer Science and Technology
A General Framework for Induction and a Study of Selective Induction
Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Bias and the probability of generalization
IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)
Journal of Artificial Intelligence Research
Multilevel data summarization from information systems: a "rule + exception" approach
AI Communications - Special issue on Artificial intelligence advances in China
Multilevel data summarization from information systems: a “rule + exception” approach
AI Communications - Artificial Intelligence Advances in China
Discernibility matrix simplification for constructing attribute reducts
Information Sciences: an International Journal
Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model
Information Sciences: an International Journal
Research on a Novel Data Mining Method Based on the Rough Sets and Neural Network
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
On reduct construction algorithms
Transactions on computational science II
A model of user-oriented reduct construction for machine learning
Transactions on rough sets VIII
Solving the attribute reduction problem with ant colony optimization
Transactions on rough sets XIII
Application of rough set theory for evaluating polysaccharides extraction
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
On reduct construction algorithms
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Extended rough set-based attribute reduction in inconsistent incomplete decision systems
Information Sciences: an International Journal
Test-cost-sensitive attribute reduction
Information Sciences: an International Journal
| Hi-index | 0.00 |
Generally a database encompasses various kinds of knowledge and is shared by many users. Different users may prefer different kinds of knowledge. So it is important for a data mining algorithm to output specific knowledge according to users' current requirements (preference). We call this kind of data mining requirement-oriented knowledge discovery (ROKD). When the rough set theory is used in data mining, the ROKD problem is how to find a reduct and corresponding rules interesting for the user. Since reducts and rules are generated in the same way, this paper only concerns with how to find a particular reduct. The user's requirement is described by an order of attributes, called attribute order, which implies the importance of attributes for the user. In the order, more important attributes are located before less important ones. Then the problem becomes how to find a reduct including those attributes anterior in the attribute order. An approach to dealing with such a problem is proposed. And its completeness for reduct is proved. After that, three kinds of attribute order are developed to describe various user requirements.