Association rules discovery is an important data mining technique which usually produces large number of rules. Subset and Superset queries are common queries for association rules. We introduce a new index structure (SSST) for querying association rules, based on a unique set representation using a hierarchical structure. It supports both Subset and Superset queries. Further, it is scalable and adapts to different types of data. The performance of SSST is evaluated using real as well as synthetic datasets, spanning dense and sparse data. The experiments showed that the proposed structure outperforms other set indexing techniques significantly, especially for dense datasets. Also, it scales well with both the number of association rules and the query size.