Mining of High-Utility Patterns in Big IoT-based Databases

Wu, Jimmy Ming‐Tai; Srivastava, Gautam; Lin, Jerry Chun‐Wei; Djenouri, Youcef; Wei, Min; Parizi, Reza M.; Khan, Mohammad S.

doi:10.1007/s11036-020-01701-5

Cited by 15 publications

(3 citation statements)

References 35 publications

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“…Meanwhile, high utility pattern mining is applied in retail analysis [27] or database analysis of IoT systems [28], [29]. In these fields, data are continuously generated and accumulated.…”

Section: Related Work a High Utility Pattern Miningmentioning

confidence: 99%

Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database

Lee,

Kim,

Cho

et al. 2024

IEEE Access

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Top-k high utility pattern mining, which extracts the highest top-k patterns that the users want to find, has been actively studied. Most previous studies in this domain have focused on static databases, where data insertions do not occur. In the real world, however, various applications continuously generate new data, and existing top-k high utility pattern mining algorithms devised to process static databases cannot handle incremental databases. Although some methods can handle stream data, they have the limitation of processing a portion of the database rather than the entire accumulated database. In this paper, we suggest an efficient incremental mining method that discovers top-k high utility patterns from the entire accumulated database. The proposed approach utilizes a list structure that stores minimal utility information required for the mining process and does not generate candidate itemsets. The suggested algorithm processes the incremental data with a single database scan and restructures the list for efficient mining. Moreover, four efficient threshold raising techniques along with a restoring technique are utilized to calculate the optimal threshold value in an accumulated incremental environment. The results of the experiments on runtime, memory, and scalability show that the suggested method efficiently processes the entire incremental database.INDEX TERMS Data analysis, High utility pattern, Incremental dynamic database, Pattern analysis, Top-k.

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“…Meanwhile, high utility pattern mining is applied in retail analysis [27] or database analysis of IoT systems [28], [29]. In these fields, data are continuously generated and accumulated.…”

Section: Related Work a High Utility Pattern Miningmentioning

confidence: 99%

Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database

Lee,

Kim,

Cho

et al. 2024

IEEE Access

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“…Since the results provide users with set of items that yields high profits. Therefore, high utility pattern mining is used VOLUME 11, 2023 to analyze retail [18], IoT systems [19], [20], operation patterns [21], and financial crises [22] where the quantity and profit of items are considered important. Starting with a traditional approach, various methods that leverage different strategies for high efficiency were proposed.…”

Section: Related Work a Utility-driven Pattern Miningmentioning

confidence: 99%

Efficient Method for Mining High Utility Occupancy Patterns Based on Indexed List Structure

et al. 2023

Self Cite

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High utility pattern mining has been proposed to improve the traditional support-based pattern mining methods that process binary databases. High utility patterns are discovered by effectively considering the quantity and importance of items. Recently, high utility occupancy pattern mining studies have been conducted to extract high-quality patterns by utilizing both the occupancy utility and frequency measure. Although the previous approaches provide worthy information in terms of utility occupancy, they require time-consuming tasks because of numerous comparison operations in exploring entries in global data structures. This results in significant performance degradation when the database is large, or a pre-defined threshold is low. An indexed list structure improves the inefficiency of the list-based approach by structurally connecting each tuple. In this paper, we propose an efficient high utility occupancy mining approach based on novel indexed list-based structures. The two newly designed data structures maintain index information on items or patterns and facilitate rapid pattern extension. Our approach improves the cost of generating long patterns of list-based ones by reducing a large number of comparison overheads. In addition, we devise novel constructing and mining methods that are suitable for the proposed data structures and utility occupancy functions. To narrow the wide search space, efficient pruning techniques apply to the designed methods. Thorough performance experiments using real and synthetic datasets show that our method is more efficient than state-of-the-art methods in environments where given thresholds change.INDEX TERMS Data mining, high-utility occupancy pattern, indexed list structure, pattern mining.

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“…[8][9][10] Furthermore, the studies that find patterns with high profits in the real world are conducted by setting the weight as the profit of each item. The examples of this include Big IoT, 11 stock market, 12 taxi, 13 and Twitter. 14 Meanwhile, Tang et al proposed an occupancy pattern mining algorithm.…”

Section: Introductionmentioning

confidence: 99%

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

Ryu

Yun

Lee

et al. 2022

Int J of Intelligent Sys

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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.

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Mining of High-Utility Patterns in Big IoT-based Databases

Cited by 15 publications

References 35 publications

Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database

Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database

Efficient Method for Mining High Utility Occupancy Patterns Based on Indexed List Structure

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

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