Boolean Feature Discovery in Empirical Learning
Machine Learning
Mining knowledge at multiple concept levels
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Multidimensional Database Technology
Computer
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Extraction of minimal decision algorithm using rough sets and genetic algorithm
Systems and Computers in Japan
Learning cross-level certain and possible rules by rough sets
Expert Systems with Applications: An International Journal
Hierarchical information system and its properties
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Hierarchical qualitative inference model with substructures
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Dynamic programming approach to optimization of approximate decision rules
Information Sciences: an International Journal
Comparison of granular computing models in a set-theoretic framework
BI'12 Proceedings of the 2012 international conference on Brain Informatics
Anonymizing classification data using rough set theory
Knowledge-Based Systems
Knowledge reduction for decision tables with attribute value taxonomies
Knowledge-Based Systems
Multi-level rough set reduction for decision rule mining
Applied Intelligence
Hi-index | 12.05 |
Decision rules mining is an important technique in machine learning and data mining. It has been studied intensively during the past few years. However, most existing algorithms are based on flat dataset, from which a set of decision rules mined may be very large for large scale data. Such a set of rules is not easily understandable and really useful for users. Moreover, too many rules may lead to over fitting. Thus, an approach to hierarchical decision rules mining is provided in this paper. It can mine decision rules from different levels of abstraction. The aim of this approach is to improve the quality and efficiency of decision rules mining by combining the hierarchical structure of multidimensional data model and the techniques of rough set theory. The approach follows the so-called separate-and-conquer strategy. It can not only provide a method of hierarchical decision rules mining, but also the most important is that it can reveal the fact that there exists property-preserving among decision rules mined from different levels, which can further improve the efficiency of decision rules mining.