C4.5: programs for machine learning
C4.5: programs for machine learning
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
IEEE Transactions on Knowledge and Data Engineering
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Reducing decision tree fragmentation through attribute value grouping: A comparative study
Intelligent Data Analysis
Reduction based symbolic value partition
ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
Weighted reduction for decision tables
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
RSKT'06 Proceedings of the First 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
Knowledge reduction in inconsistent decision tables
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Reduction based symbolic value partition
ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
Dynamic discreduction using Rough Sets
Applied Soft Computing
Covering numbers in covering-based rough sets
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Transversal and function matroidal structures of covering-based rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Knowledge acquisition in inconsistent multi-scale decision systems
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Rehabilitation and reconstruction of asphalts pavement decision making based on rough set theory
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part II
Quantitative analysis for covering-based rough sets through the upper approximation number
Information Sciences: an International Journal
Four matroidal structures of covering and their relationships with rough sets
International Journal of Approximate Reasoning
Incorporating logistic regression to decision-theoretic rough sets for classifications
International Journal of Approximate Reasoning
Feature selection with test cost constraint
International Journal of Approximate Reasoning
Knowledge reduction for decision tables with attribute value taxonomies
Knowledge-Based Systems
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In data mining, searching for simple representations of knowledge is a very important issue. Attribute reduction, continuous attribute discretization and symbolic value partition are three preprocessing techniques which are used in this regard. This paper investigates the symbolic value partition technique, which divides each attribute domain of a data table into a family for disjoint subsets, and constructs a new data table with fewer attributes and smaller attribute domains. Specifically, we investigates the optimal symbolic value partition (OSVP) problem of supervised data, where the optimal metric is defined by the cardinality sum of new attribute domains. We propose the concept of partition reducts for this problem. An optimal partition reduct is the solution to the OSVP-problem. We develop a greedy algorithm to search for a suboptimal partition reduct, and analyze major properties of the proposed algorithm. Empirical studies on various datasets from the UCI library show that our algorithm effectively reduces the size of attribute domains. Furthermore, it assists in computing smaller rule sets with better coverage compared with the attribute reduction approach.