WisRule: First cognitive algorithm of wise association rule mining

Shaheen, Muhammad

doi:10.1177/01655515221108695

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…IoT architectures are ideal for the acquisition and management of data from large buildings [10][11][12] . The studies discuss the application of a Smart City Model to a traditional university campus using IoT and big data, aiming to improve sustainability, resource management, and urban comfort.…”

Section: Association Rule Mining In the Field Of Energy Building Mana...mentioning

confidence: 99%

A big data association rule mining based approach for energy building behaviour analysis in an IoT environment

Dolores,

Fernandez-Basso,

Gómez-Romero

et al. 2023

Sci Rep

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The enormous amount of data generated by sensors and other data sources in modern grid management systems requires new infrastructures, such as IoT (Internet of Things) and Big Data architectures. This, in combination with Data Mining techniques, allows the management and processing of all these heterogeneous massive data in order to discover new insights that can help to reduce the energy consumption of the building. In this paper, we describe a developed methodology for an Internet of Things (IoT) system based on a robust big data architecture. This innovative approach, combined with the power of Spark algorithms, has been proven to uncover rules representing hidden connections and patterns in the data extracted from a building in Bucharest. These uncovered patterns were essential for improving the building’s energy efficiency.

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Section: Association Rule Mining In the Field Of Energy Building Mana...mentioning

confidence: 99%

A big data association rule mining based approach for energy building behaviour analysis in an IoT environment

Dolores,

Fernandez-Basso,

Gómez-Romero

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Te work in reference [21] incorporated frequent itemsets with domain knowledge in the form of a taxonomy to mine negative association rules. Shaheen and Abdullah developed a series of algorithms for diferent felds, such as exploring positive and negative context-based association rules for conventional/characteristic data [24,25], and mining context-based association rules on microbial databases to extract interesting and useful associations of microbial attributes with existence of hydrocarbon reserve [26][27][28][29]. It should be noted that some contradictory rules may be mined when positive and negative rules are mined simultaneously, such as A ⇒ B and A ⇒ ¬B are both strong rules [30][31][32].…”

Section: Pnars Mining Techniquesmentioning

confidence: 99%

An Optimized Association Rules Mining Framework for Chinese Social Insurance Fund Data Auditing

Xiuguo,

Shengyong

2023

International Journal of Intelligent Systems

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Association rules mining with the Chinese social insurance fund dataset can effectively discover different kinds of errors, irregularities, and illegal acts by providing auditors with relationships among the items and therefore improve auditing quality and efficiency. However, traditional positive and negative association rules (PNARs) mining algorithms inevitably produce too many meaningless or contradictory rules when these two types of rules are mined simultaneously, which brings a huge challenge to auditors retrieving decision information. Aimed to reduce the quantity of low-reliability PNARs without missing interesting rules, this paper first proposes an improved PNARs mining algorithm with minimum correlation and triple confidence threshold to control the mined rules number by narrowing the range of confidence settings. Then, a novel pruning algorithm based on the inclusion relation of the rule’s antecedent and consequent is given to remove those redundant rules. After that, the proposed optimized PNARs mining approach is applied to the Chinese social insurance fund dataset starting with audit features influence factors mining using the Hash table. The experimental results with different datasets show that the proposed framework not only can ensure effective and interesting rules extraction but also has better performance than traditional approaches in both accuracy and efficiency, reducing the number of redundant PNARs by over 70.1% with experimental datasets and average 78.5% with auditing data mining, respectively.

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“…Extracting valuable information 8 , 9 and knowledge from large datasets 10 – 12 requires the use of various approaches such as statistics, machine learning, artificial intelligence, and database technology to find patterns, associations 13 – 16 , and rules in diverse areas 17 – 19 . Zadeh 20 utilized association rule mining methods to explore potential drugs or drug combinations associated with newly diagnosed diabetes.…”

Section: Introductionmentioning

confidence: 99%

Improved adaptive-phase fuzzy high utility pattern mining algorithm based on tree-list structure for intelligent decision systems

Chen,

Liu,

Zhang

et al. 2024

Sci Rep

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With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average.

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WisRule: First cognitive algorithm of wise association rule mining

Cited by 5 publications

References 35 publications

A big data association rule mining based approach for energy building behaviour analysis in an IoT environment

A big data association rule mining based approach for energy building behaviour analysis in an IoT environment

An Optimized Association Rules Mining Framework for Chinese Social Insurance Fund Data Auditing

Improved adaptive-phase fuzzy high utility pattern mining algorithm based on tree-list structure for intelligent decision systems

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