期刊
IEEE ACCESS
卷 10, 期 -, 页码 1419-1431出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3139028
关键词
Reliability; Itemsets; Data mining; Prediction algorithms; Correlation; Time measurement; Weight measurement; Data mining; reliable high-utility itemset; utility mining; pruning strategy
This paper introduces a new problem of mining reliable high utility patterns and proposes an efficient approach called RUPM to solve it. Experimental results show that compared to traditional algorithms, the RUPM approach can obtain patterns with higher reliability and provide pruning strategies to reduce runtime and memory usage.
Utility mining is one of the most thriving research topics with a wide range of real-world applications. High utility pattern mining uses a utility function to extract all desired patterns that exceed a minimum utility threshold. However, a significant number of patterns will be generated if this threshold is set too low, which is an inherent limitation of these algorithms. This may cause the mining process to be inefficient as it would be difficult to analyze the patterns found. Furthermore, most of these patterns are unreliable and hard to be employed in making decisions. This paper proposed a novel problem of mining reliable high utility patterns by adapting the concept of reliability to mine a significant type of pattern called reliable high utility patterns. To address this issue, an efficient approach named RUPM (Reliable Utility-based Pattern Mining) is presented. RUPM introduces three novel measurements for estimating the reliability of utility-based patterns and proposes several strategies to efficiently handle reliable patterns with high utility values. Experimental results suggest that up to 99% of the patterns discovered by existing traditional high utility pattern mining algorithms were, in fact, unreliable. In contrast, the average reliability proportion in the resultant patterns obtained from the RUPM approach is at least 47.6% higher. Moreover, the proposed pruning strategies provide a reduction in both the runtime and memory usage.
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