A hybrid of conceptual clusters, rough sets and attribute oriented induction for inducing symbolic rules

Rule induction is a data mining process for acquiring knowledge in terms of symbolic decision rules from a number of specific ´examples´ to explain the inherent causal relationship between conditional factors and a given decision/outcome. We present a Decision Rule Acquisition Workbench (DRAW) that discovers conjunctive normal form decision rules from un-annotated data-sets. Our rule-induction strategy uses (i) conceptual clustering to cluster and generate a conceptual hierarchy of the data-set; (ii) rough sets based rule induction algorithm to generate decision rules from the emergent data clusters; and (iii) attribute oriented induction to generalize the derived decision rules to yield high-level decision rules and a minimal rule-set size.