Improving pattern discovery relevancy by deriving constraints from expert models

To support knowledge discovery from data, many pattern mining techniques have been proposed. One of the bottlenecks for their dissemination is the number of computed patterns that appear to be either trivial or uninteresting with respect to available knowledge. Integration of domain knowledge in constraint-based data mining is limited. Relevant patterns still miss because methods partly fail in assessing their subjective interestingness. However, in practice, we often have in the literature mathematical models defined by experts based on their domain knowledge. We propose here to exploit such models to derive constraints that can be used during the data mining phase to improve both pattern relevancy and computational efficiency. Even though the approach is generic, it is illustrated on pattern set discovery from real data for studying soil erosion.