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.

提出了一种名为规则 Trie 的新型数据结构，用于存储关联规则挖掘生成的规则集。该数据结构是一个前缀树图结构，将规则存储为路径，相似规则在路径上重叠。实验证明了该表示技术的潜力，具有几乎没有数据损失的规则集压缩效果，以及在搜索特定规则和排序等基本操作方面的时间优势，进而在遍历时间方面取得了 8 倍的提升。