Algorithms for clustering data
Algorithms for clustering data
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Classifier fitness based on accuracy
Evolutionary Computation
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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XCS is a stochastic algorithm, so it does not guarantee to produce the same results when run with the same input. When interpretability matters, obtaining a single, stable result is important. We propose an algorithm which applies clustering in order to merge the rules produced from many XCS runs. Such an algorithm needs a measure of distance between rules; we then suggest a general definition for such a measure. We finally evaluate the results obtained on two well-known data sets, with respect to performance and stability. We find that stability is improved, while performance is slightly impaired.