Mining Rare Utility Patterns within Target Items

As a crucial subfield of pattern discovery, high utility rare itemset mining (HURIM) is developed to discover abnormal but significant patterns. HURIM plays a vital role in various scenarios, such as network security, disease detection, and biomedicine. However, the traditional HURIM algorithms ignore the users’ demands, which generates massive needless patterns. In general, target-based HURIM algorithms can discover more useful information that meets the needs of users than traditional HURIM algorithms. To this end, we propose a targeted HURIM algorithm called Mining Rare Utility Patterns within Target Items (TIRUP). TIRUP adopts two techniques (projection and merging technologies), to diminish the consumption of database scanning. To effectively improve the performance of TIRUP, this paper utilizes several strategies based on frequency, utility, and target factors. Finally, a series of experiments are conducted to demonstrate the efficiency of the proposed TIRUP algorithm, and the experimental results indicate that TIRUP is suitable for processing large-scale and dense datasets.