The Use of Galois Lattices to Fine-Tune a Meta-Classifier

This paper deals with the problem of mining very large distributed databases. We propose a distributed data mining technique which produces a meta-classifier that is both predictive and descriptive. This meta-classifier is in the form of a set of classification rules, which could be refined then validated by fine-tuning its rule set using a concept lattice. A detailed description of this method is presented in the paper, as well as the experimentation proving the viability of our technique and the usefulness of using a concept lattice to validate rules of a meta-classifier.