Mining generalized association rules
Future Generation Computer Systems - Special double issue on data mining
Rough computational methods for information systems
Artificial Intelligence
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data
Knowledge and Information Systems
Utilizing hierarchical feature domain values for prediction
Data & Knowledge Engineering
Learning cross-level certain and possible rules by rough sets
Expert Systems with Applications: An International Journal
Converse approximation and rule extraction from decision tables in rough set theory
Computers & Mathematics with Applications
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Learning decision trees with taxonomy of propositionalized attributes
Pattern Recognition
Attribute Value Taxonomy Generation through Matrix Based Adaptive Genetic Algorithm
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Fuzzy rough sets with hierarchical quantitative attributes
Expert Systems with Applications: An International Journal
Attribute reduction and optimal decision rules acquisition for continuous valued information systems
Information Sciences: an International Journal
Hierarchical decision rules mining
Expert Systems with Applications: An International Journal
A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction
IEEE Transactions on Knowledge and Data Engineering
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Rough set based approaches to feature selection for Case-Based Reasoning classifiers
Pattern Recognition Letters
Building a highly-compact and accurate associative classifier
Applied Intelligence
Hybrid ensemble approach for classification
Applied Intelligence
A vague-rough set approach for uncertain knowledge acquisition
Knowledge-Based Systems
Propositionalized attribute taxonomies from data for data-driven construction of concise classifiers
Expert Systems with Applications: An International Journal
Rough sets for adapting wavelet neural networks as a new classifier system
Applied Intelligence
Feature subset selection wrapper based on mutual information and rough sets
Expert Systems with Applications: An International Journal
From data to global generalized knowledge
Decision Support Systems
Cluster-based instance selection for machine classification
Knowledge and Information Systems
A general insight into the effect of neuron structure on classification
Knowledge and Information Systems
Improvement of neural network classifier using floating centroids
Knowledge and Information Systems
Graded rough set model based on two universes and its properties
Knowledge-Based Systems
A novel feature selection method based on normalized mutual information
Applied Intelligence
Anonymizing classification data using rough set theory
Knowledge-Based Systems
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Most previous studies on rough sets focused on attribute reduction and decision rule mining on a single concept level. Data with attribute value taxonomies (AVTs) are, however, commonly seen in real-world applications. In this paper, we extend Pawlak's rough set model, and propose a novel multi-level rough set model (MLRS) based on AVTs and a full-subtree generalization scheme. Paralleling with Pawlak's rough set model, some conclusions related to the MLRS are given. Meanwhile, a novel concept of cut reduction based on MLRS is presented. A cut reduction can induce the most abstract multi-level decision table with the same classification ability on the raw decision table, and no other multi-level decision table exists that is more abstract. Furthermore, the relationships between attribute reduction in Pawlak's rough set model and cut reduction in MLRS are discussed. We also prove that the problem of cut reduction generation is NP-hard, and develop a heuristic algorithm named CRTDR for computing the cut reduction. Finally, an approach named RMTDR for mining multi-level decision rule is provided. It can mine decision rules from different concept levels. Example analysis and comparative experiments show that the proposed methods are efficient and effective in handling the problems where data is associated with AVTs.