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Data mining has been actively studied, and it has become more important due to the development of information technology and the demands of diverse applications, such as the retail market, medical field, and manufacturing plants. High average-utility pattern mining is a type of data mining that finds valuable patterns by dividing the utility of a pattern by the length of the pattern. It considers the length of the pattern, so it can handle the demerits of utility pattern mining and extract more important patterns. Meanwhile, stream data is produced in real time, and the importance of the recent data gradually increases as opposed to the old data. However, most of the previous high average utility pattern mining approaches cannot find the latest valuable patterns over the data streams. Although research using a sliding window model was conducted to handle only the recent data, it has a problem due to the data structure. Since the previous work is a tree-based approach, numerous candidate patterns are generated, and an additional database scan is required to calculate the actual utilities of candidates. Creating the candidate patterns reduces the efficiency of runtime and memory and makes real-time data processing difficult. We propose an efficient algorithm that uses a list structure for sliding window-based high average-utility pattern mining in this paper. The proposed algorithm scans the stream data once, mines the complete set of the recent high average-utility patterns without generating candidates, and reduces the search space by using a novel pruning technique. The experiment results show that the proposed algorithm outperforms the state-of-the-art algorithms in terms of the runtime, memory, and scalability in real and synthetic datasets. [ABSTRACT FROM AUTHOR]

High utility pattern mining (HUPM) discovers meaningful patterns by considering features of items and utility from non-binary data. Data called stream data is continually generated over time. Various techniques based on high utility pattern mining have been suggested for processing stream data. High utility pattern mining based on a sliding window performs pattern mining using a window. Since it uses only the data stored in a window, only the latest data can be managed. However, Stream data has the property that newly created data has a higher influence than relatively old data. It is necessary to consider the importance of the data stored in a window differently. In this paper, we propose an efficient algorithm based on a sliding window approach that mines high utility patterns considering the latest data more significantly from damped stream data where new data is constantly being inserted. In other words, our technique divides the stream data into fixed-sized multiple batch data and processes differently the importance of each batch data in a window according to the added time using the decaying factor. Moreover, we conduct experiments to compare and analyze our approach with the state-of-the-art algorithms using real and synthetic datasets. The experimental results show that our proposed method outperforms the competitors in terms of run time, memory usage, and scalability test. [ABSTRACT FROM AUTHOR]