  Association rule mining techniques can generate a large volume of sequential
data when implemented on transactional databases. Extracting insights from a
large set of association rules has been found to be a challenging process. When
examining a ruleset, the fundamental question is how to summarise and represent
meaningful mined knowledge efficiently. Many algorithms and strategies have
been developed to address issue of knowledge extraction; however, the
effectiveness of this process can be limited by the data structures. A better
data structure can sufficiently affect the speed of the knowledge extraction
process. This paper proposes a novel data structure, called the Trie of rules,
for storing a ruleset that is generated by association rule mining. The
resulting data structure is a prefix-tree graph structure made of pre-mined
rules. This graph stores the rules as paths within the prefix-tree in a way
that similar rules overlay each other. Each node in the tree represents a rule
where a consequent is this node, and an antecedent is a path from this node to
the root of the tree. The evaluation showed that the proposed representation
technique is promising. It compresses a ruleset with almost no data loss and
benefits in terms of time for basic operations such as searching for a specific
rule and sorting, which is the base for many knowledge discovery methods.
Moreover, our method demonstrated a significant improvement in traversing time,
achieving an 8-fold increase compared to traditional data structures.
