Efficient Mining of Pareto-Front High Expected Utility Patterns

In this paper, we present a model called MHEUPM to efficiently mine the interesting high expected utility patterns (HEUPs) by employing the multi-objective evolutionary framework. The model considers both uncertainty and utility factors to discover meaningful HEUPMs without requiring pre-defined threshold values (such as minimum utility and minimum uncertainty). The effectiveness of the model is validated using two encoding methodologies. The proposed MHEUPM model can discover a set of HEUPs within a limited period. The efficiency of the proposed model is determined through rigorous analysis and compared to the standard pattern-mining methods in terms of hypervolume, convergence, and number of the discovered patterns.