Semantic analysis of association rules

When applying association mining to real datasets, a ma-jor obstacle is that often a huge number of rules are gen-erated even with very reasonable support and confidence. Among these rules, many are trivial, redundant, semantically wrong, or already known by end-users. Association rule post-processing aims to remove these undesired rules. Existing work mainly focuses on reducing redundant or finding unex-pected association rules. In this paper, we propose an inno-vative method based on semantic network. We semantically divide association rules into five categories: trivial, known and correct, unknown and correct, known and incorrect, un-known and incorrect. Our method can be efficiently inte-grated with existing rule reduction techniques to construct a concise, high-quality, and user-specific association rule set. We evaluate our approach on a real public-health dataset, the Heartfelt study, and we can prune off 97.81 % of association rules that are trivial or incorrect. The remaining rules are confirmed by either health science literature or a high-quality biomedical knowledge base