FRI-Miner: Fuzzy Rare Itemset Mining
Yanling Cui, Wensheng Gan, Hong Lin, and Weimin Zheng

TL;DR
FRI-Miner is a novel fuzzy-based algorithm designed to efficiently discover valuable rare itemsets in transaction databases, outperforming existing methods in effectiveness and efficiency by incorporating fuzzy theory and pruning strategies.
Contribution
It introduces a new fuzzy rare itemset mining algorithm that effectively finds interesting patterns considering quantitative data and fuzzy semantics.
Findings
Outperforms state-of-the-art algorithms in effectiveness and efficiency.
Discovers fewer, more interesting fuzzy rare itemsets.
Utilizes fuzzy-list structure and pruning strategies to reduce search space.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Mining Algorithms and Applications · Rough Sets and Fuzzy Logic · Imbalanced Data Classification Techniques
