Modern Applications and Challenges for Rare Itemset Mining

Data mining is the process of extracting useful unknown knowledge from large datasets. Frequent itemset fundamental task data that aims at discovering interesting itemsets frequently appear together in a dataset. However, infrequent (rare) may be more many real-life applications such as predicting telecommunication equipment failures, genetics, medical diagnosis, or anomaly detection. In this paper, we survey up-to-date methods rare mining. The main goal to provide comprehensive overview state-of-the-art algorithms and its applications. contributions can summarized follows. first part, define by explaining key concepts terminology, motivation examples, comparisons with underlying concepts. Then, highlight state-of-art for Furthermore, present variations discuss limitations traditional algorithms. After that, last, point out research opportunities challenges future research.