Mining Maximal High Utility Itemsets on Dynamic Profit Databases

Nguyen, Loan T. T.; Vu, Dinh-Bao; Nguyen, Trinh Duy; Vo, Bay

doi:10.1080/01969722.2019.1705549

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…In fraction (12), the numerator is obtained by adding the aggregated information of X . If the sum of sumUO and sum-RUO divided by ⌈minSup (QDB, γ )⌉ is less than a minimum utility occupancy threshold, any super patterns need not be confirmed.…”

Section: Novel Methods For Mining High Utility Occupancy Patterns Wit...mentioning

confidence: 99%

“…Therefore, high utility pattern mining is utilized in various applications [10]. Moreover, expanded research on high utility pattern mining has been performed by combining the utility concept with approaches based on other notions, such as sequential pattern mining [11], maximal pattern mining [12], fuzzy pattern mining [13], and non-redundant pattern mining [14]. However, high utility pattern mining cannot fully reflect the frequency of the pattern.…”

Section: Is Amentioning

confidence: 99%

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

et al. 2023

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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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Section: Novel Methods For Mining High Utility Occupancy Patterns Wit...mentioning

confidence: 99%

Section: Is Amentioning

confidence: 99%

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

et al. 2023

Self Cite

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“…Moreover, there are many methods for other dynamic scenarios. For example, M-PM [26] and SOHUPDS [27] for dealing with data streams, iMEFIM [28] and CHUI-Power [29] for processing the dynamic unit profit databases. As the common and basic situation of dynamic change, incremental high utility mining is still our focus.…”

Section: Related Workmentioning

confidence: 99%

IDHUP: Incremental Discovery of High Utility Pattern

Yu¹,

Yu²,

Gan³

et al. 2023

Journal of Internet Technology

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<p>As a sub-problem of pattern discovery, utility-oriented pattern mining has recently emerged as a focus of researchers’ attention and offers broad application prospects. Considering the dynamic characteristics of the input databases, incremental utility mining methods have been proposed, aiming to discover implicit information/ patterns whose importance/utility is not less than a user-specified threshold from incremental databases. However, due to the explosive growth of the search space, most existing methods perform unsatisfactorily under the low utility threshold, so there is still room for improvement in terms of running efficiency and pruning capacity. Motivated by this, we provide an effective and efficient method called IDHUP by designing an indexed partitioned utility list structure and employing four pruning strategies. With the proposed data structure, IDHUP can not only dynamically update the utility values of patterns but also avoid visiting non-occurred patterns. Moreover, to further exclude ineligible patterns and avoid unnecessary exploration, we put forward the remaining utility reducing strategy and three other revised pruning strategies. Experiments on various datasets demonstrated that the designed IDHUP algorithm has the best performance in terms of running time compared to state-of-the-art algorithms.</p> <p> </p>

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“…Wu et al 26 proposed a method that uses distributed computing to quickly find the utility patterns in large amounts of data. As examples of combining with other fields, utility pattern mining approaches 27–29 in a profit database where the weight of the items fluctuates with transaction are proposed. PUSP 30 and SU‐Chain 31 extract the utility patterns by considering the passage of time and the order that the items appeared.…”

Section: Related Workmentioning

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 Maximal High Utility Itemsets on Dynamic Profit Databases

Cited by 13 publications

References 23 publications

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

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

IDHUP: Incremental Discovery of High Utility Pattern

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

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