Abstract: The high utility frequent pattern mining algorithm mines more important frequent patterns from the data by using the importance degree  information. On this basis, the high utility quantitative frequent pattern mining algorithm further explores the quantitative relationship between data items, and thus has become a popular research topic in the field of data mining. RHUQI-Miner is proposed to improve the performance and practicability of the algorithm. Firstly, the concept of related degree is proposed, the item related degree structure is constructed according to the related degree, and a pruning optimization strategy is given to find frequent patterns with higher related degree, reducing redundancy and invalid frequent patterns. Secondly, the fixed pattern length strategy is used to modify the utility information of the item in the mining process, so that the algorithm can control the length of the output frequent pattern according to the actual data situation, and further improve the performance and practicability of the algorithm. The experimental results show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process, which can provide data support for differentiated and precise maintenance strategies.

Key words: high utility, quantitative, frequent pattern mining, related pruning, fixed pattern length