Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

• Published in: International journal of intelligent systems Vol. 37; no. 9; pp. 5477 - 5507
• Main Authors: Ryu, Taewoong, Yun, Unil, Lee, Chanhee, Lin, Jerry Chun‐Wei, Pedrycz, Witold
• Format: Journal Article
• Language: English
• Published: New York John Wiley & Sons, Inc 01.09.2022
• Subjects: Algorithms; Data mining; Data structures; high‐utility occupancy pattern; incremental database; Intelligent systems; Pattern analysis; pattern mining; Performance evaluation
• ISSN: 0884-8173; 1098-111X
• DOI: 10.1002/int.22799

Utility pattern mining is a branch of data mining that extracts valid patterns by considering the quantity and weight of the items. In addition, utility occupancy pattern mining, which considers the quantity, importance, and proportion of the pattern in the transaction, has been proposed. Despite this advantage, there is no utility seizing approach to handle the dynamically generated data flows. As electronics are interconnected and intelligent systems are constructed, data is generated in real‐time and accumulated rapidly. Therefore, a method to read data immediately in a dynamic environment and efficiently analyze massive data is required. To overcome the limitations of the existing utility occupancy methods, we propose a novel mining approach, HUOMI, which performs quickly on an increasing database. The suggested algorithm has an optimized data structure and an improved pruning technique, which can respond to the dynamic environment promptly. To indicate the effectiveness of the proposed method, performance evaluations were conducted on real and synthetic data sets. In the experimental results, the suggested algorithm showed a better performance than the other state‐of‐the‐art algorithms.