Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Triggered Rule Discovery in Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
A Modified Classifier System Compaction Algorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
For real! XCS with continuous-valued inputs
Evolutionary Computation
Applications of Learning Classifier Systems
Applications of Learning Classifier Systems
Classifier fitness based on accuracy
Evolutionary Computation
Mining comprehensible clustering rules with an evolutionary algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Classifier Conditions Using Gene Expression Programming
Learning Classifier Systems
Clustering with XCS on Complex Structure Dataset
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Clustering with XCS and agglomerative rule merging
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Self-adaptation of learning rate in XCS working in noisy and dynamic environments
Computers in Human Behavior
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This paper presents a novel approach to clustering using an accuracy-based Learning Classifier System. Our approach achieves this by exploiting the generalization mechanisms inherent to such systems. The purpose of the work is to develop an approach to learning rules which accurately describe clusters without prior assumptions as to their number within a given dataset. Favourable comparisons to the commonly used k-means algorithm are demonstrated on a number of synthetic datasets.