Efficient list based mining of high average utility patterns with maximum average pruning strategies

Kim, Heonho; Yun, Unil; Baek, Yoonji; Kim, Jong‐Seong; Vo, Bay; Yoon, Eunchul; Fujita, Hamido

doi:10.1016/j.ins.2020.07.043

Cited by 48 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High utility pattern mining has been evolved in a variety of ways to reflect real world factor or scenarios. High average utility pattern mining [32], [33], [34] considers the utility and length of the pattern simultaneously. The pre-large concept [35], [36] finds large and pre-large patterns to minimize database scans.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Considering the protection of sensitive content, Le et al designed an algorithm H-FHAUI [58] with a novel vertical utility list structure, TIU-VIU, for updating upper bounds quickly. Kim et al [59] employed the maximum remaining average-utility upper bound and tight maximum average-utility upper bound and proposed a HAUPM approach with the list-based structure.…”

Section: High Average-utility Pattern Miningmentioning

confidence: 99%

Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

Cao,

Duan

2023

Applied Sciences

View full text Add to dashboard Cite

High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) also have a high utility that increases with its length. Therefore, it is difficult to obtain diverse patterns of resources. The patterns that consist of many low-utility items can also be a HUSP. In practice, such a long pattern is difficult to analyze. In addition, the low-utility items do not always reflect the interestingness of association rules. High average-utility pattern mining is considered a solution to extract more significant patterns by considering the lengths of patterns. In this paper, we formulate the problem of top-k high average-utility sequential pattern mining (HAUSPM) and propose a novel algorithm for resource transformation. We adopt a projection mechanism to improve efficiency. We also adopt the sequence average-utility-raising strategy to increase thresholds. We design the prefix extension average utility and the reduced sequence average utility by incorporating the average utility into the utility upper bounds. The results of our comparative experiments demonstrate that the proposed algorithm can achieve sufficiently good performance.

show abstract

“…In this section, the definitions used in the existing pattern mining [46][47][48] and suggested research are explained. Table 1 is the incremental database that is used as an example.…”

Section: Preliminariesmentioning

confidence: 99%

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

Ryu

Yun

Lee

et al. 2022

Int J of Intelligent Sys

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Efficient list based mining of high average utility patterns with maximum average pruning strategies

Cited by 48 publications

References 42 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

Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

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

Contact Info

Product

Resources

About