International Journal of Man-Machine Studies
Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Comparison of neofuzzy and rough neural networks
Information Sciences: an International Journal
Maintaining knowledge about temporal intervals
Communications of the ACM
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Genetic Algorithms
Information Granules in Distributed Environment
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Time Complexity of Rough Clustering: GAs versus K-Means
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Artificial Intelligence Review
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences—Informatics and Computer Science: An International Journal
Web Intelligence and Agent Systems
MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & Knowledge Engineering
ECM: An evidential version of the fuzzy c-means algorithm
Pattern Recognition
Precision of Rough Set Clustering
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Improved Classification for Problem Involving Overlapping Patterns
IEICE - Transactions on Information and Systems
Evolutionary Rough K-Means Clustering
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences: an International Journal
Rough sets and near sets in medical imaging: a review
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Transactions on rough sets VI
Applications of rough set based K-means, Kohonen SOM, GA clustering
Transactions on rough sets VII
Comparison of conventional and rough K-means clustering
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Comparing clustering schemes at two levels of granularity for mobile call mining
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Review: Hybrid expert systems: A survey of current approaches and applications
Expert Systems with Applications: An International Journal
Crisp and soft clustering of mobile calls
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Iterative meta-clustering through granular hierarchy of supermarket customers and products
Information Sciences: an International Journal
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The rough set is a useful notion for the classification of objects when the available information is not adequate to represent classes using precise sets. Rough sets have been successfully used in information systems for learning rules from an expert. This paper describes how genetic algorithms can be used to develop rough sets. The proposed rough set theoretic genetic encoding will be especially useful in unsupervised learning. A rough set genome consists of upper and lower bounds for sets in a partition. The partition may be as simple as the conventional expert class and its complement or a more general classification scheme. The paper provides a complete description of design and implementation of rough set genomes. The proposed design and implementation is used to provide an unsupervised rough set classification of highway sections.