  Data mining is a widely used technology for various real-life applications of
data analytics and is important to discover valuable association rules in
transaction databases. Interesting itemset mining plays an important role in
many real-life applications, such as market, e-commerce, finance, and medical
treatment. To date, various data mining algorithms based on frequent patterns
have been widely studied, but there are a few algorithms that focus on mining
infrequent or rare patterns. In some cases, infrequent or rare itemsets and
rare association rules also play an important role in real-life applications.
In this paper, we introduce a novel fuzzy-based rare itemset mining algorithm
called FRI-Miner, which discovers valuable and interesting fuzzy rare itemsets
in a quantitative database by applying fuzzy theory with linguistic meaning.
Additionally, FRI-Miner utilizes the fuzzy-list structure to store important
information and applies several pruning strategies to reduce the search space.
The experimental results show that the proposed FRI-Miner algorithm can
discover fewer and more interesting itemsets by considering the quantitative
value in reality. Moreover, it significantly outperforms state-of-the-art
algorithms in terms of effectiveness (w.r.t. different types of derived
patterns) and efficiency (w.r.t. running time and memory usage).
