We present a general encoding scheme for a wide class of problems (including among others such problems like data reduction, feature selection, feature extraction, decision rules generation, pattern extraction from data or conflict resolution in multi-agent systems) and we show how to combine it with a propositional (Boolean) reasoning to develop efficient heuristics searching for (approximate) solutions of these problems. We illustrate our approach by examples, we show some experimental results and compare them with those reported in literature. We also show that association rule generation is strongly related with reduct approximation.
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