Finding spectrum occupancy pattern using CBFPP mining technique

Karthik, G. M.; Sayeekumar, M.; Kumaravel, R.; Aravind, T.

doi:10.3233/jifs-200368

Cited by 3 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HOMI [50] extracts high occupancy patterns from an incremental database using the list structure. CFBPP [51] proposes tire-based occupancy pattern mining to discover licensed empty spectrum patterns. QFWO algorithm [52] is a listbased algorithm.…”

Section: B Occupancy-driven Pattern Miningmentioning

confidence: 99%

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

et al. 2023

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: B Occupancy-driven Pattern Miningmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

show abstract

“…This method considers the share and the influence of the item in the transaction, so it extracts more meaningful patterns 16 . The examples of real‐world applications utilizing occupancy pattern mining are wireless spectrum 17 and spectrum 18 . Still, occupancy pattern mining cannot express the quantity or the weight of an item.…”

Section: Introductionmentioning

confidence: 99%

“…16 The examples of real-world applications utilizing occupancy pattern mining are wireless spectrum 17 and spectrum. 18 Still, occupancy pattern mining cannot express the quantity or the weight of an item. To accommodate these requirements, the OCEAN, 19 the first approach to high utility occupancy pattern mining, is proposed.…”

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

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

Advanced approach for mining utility occupancy patterns in incremental environment

Cho,

Kim,

Park

et al. 2024

Knowledge-Based Systems

View full text Add to dashboard Cite

Finding spectrum occupancy pattern using CBFPP mining technique

Cited by 3 publications

References 27 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

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

Advanced approach for mining utility occupancy patterns in incremental environment

Contact Info

Product

Resources

About