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

Ryu, Taewoong; Yun, Unil; Lee, Chanhee; Lin, Jerry Chun‐Wei; Pedrycz, Witold

doi:10.1002/int.22799

Cited by 15 publications

(2 citation statements)

References 45 publications

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“…It utilized a utility tolerance factor to extract approximate high utility patterns from a noisy database. After the HUOMI algorithm [ 33 ] was proposed, which used an optimized data structure and an improved pruning technique to respond to the dynamic environment promptly.…”

Section: Related Workmentioning

confidence: 99%

Mining actionable combined high utility incremental and associated sequential patterns

Shi

Gong

et al. 2023

PLoS ONE

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High utility sequential pattern (HUSP) mining aims to mine actionable patterns with high utilities, widely applied in real-world learning scenarios such as market basket analysis, scenic route planning and click-stream analysis. The existing HUSP mining algorithms mainly attempt to improve computation efficiency while maintaining the algorithm stability in the setting of large-scale data. Although these methods have made some progress, they ignore the relationship between additional items and underlying sequences, which directly leads to the generation of redundant sequential patterns sharing the same underlying sequence. Hence, the mined patterns’ actionability is limited, which significantly compromises the performance of patterns in real-world applications. To address this problem, we present a new method named Combined Utility-Association Sequential Pattern Mining (CUASPM) by incorporating item/sequence relations, which can effectively remove redundant patterns and extract high discriminative and strongly associated sequential pattern combinations with high utilities. Specifically, we introduce the concept of actionable combined mining into HUSP mining for the first time and develop a novel tree structure to select discriminative high utility sequential patterns (HUSPs) for downstream tasks. Furthermore, two efficient strategies (i.e., global and local strategies) are presented to facilitate mining HUSPs while guaranteeing utility growth and high levels of association. Last, two parameters are introduced to evaluate the interestingness of patterns to choose the most useful actionable combined HUSPs (ACHUSPs). Extensive experimental results demonstrate that the proposed CUASPM outperforms the baselines in terms of execution time, memory usage, mining high discriminative and strongly associated HUSPs.

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Section: Related Workmentioning

confidence: 99%

Mining actionable combined high utility incremental and associated sequential patterns

Shi

Gong

et al. 2023

PLoS ONE

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“…SHUO-FI [53] finds utility occupancy patterns considering distance constraints. UHUOPM [54] and HUOMI [55] adopt the list structure and extract utility occupancy patterns from uncertain situations and incremental environments, respectively. pnHUO [56] is an algorithm of the research that defined a new problem by integrating utility occupancy and negative utility concepts.…”

Section: B Occupancy-driven Pattern Miningmentioning

confidence: 99%

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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A Survey on Occupancy-Based Pattern Mining

Inaganti,

Saleti

2024

Lecture Notes in Networks and Systems

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

Cited by 15 publications

References 45 publications

Mining actionable combined high utility incremental and associated sequential patterns

Mining actionable combined high utility incremental and associated sequential patterns

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

A Survey on Occupancy-Based Pattern Mining

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