Article
Computer Science, Artificial Intelligence
Mahmoud A. Mahdi, Khalid M. Hosny, Ibrahim Elhenawy
Summary: The paper introduces a new algorithm called FR-Tree for mining rare association rules, demonstrating its advantage in extracting high confidence rare association rules.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jose A. Diaz-Garcia, M. Dolores Ruiz, Maria J. Martin-Bautista
Summary: This paper discusses the problem of social media mining and the application of unsupervised techniques, particularly association rules. It provides a broad overview of the applications of association rules in social media mining, focusing on their application to mining textual entities such as tweets. The strengths and weaknesses of using association rules for different tasks in textual social media are also discussed. Finally, the paper provides a perspective on the challenges that association rules will face in the next decade within the field of social media mining.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Giuseppe Agapito, Pietro Hiram Guzzi, Mario Cannataro
Summary: Association rule mining (ARM) is widely used in various fields, but different datasets require different algorithms. This work introduces a new ARM algorithm, BPARES, which improves performance and reduces memory consumption through parallel computing and balancing strategies.
INFORMATION SCIENCES
(2021)
Article
Mathematics
Oleg Gorokhov, Mikhail Petrovskiy, Igor Mashechkin, Maria Kazachuk
Summary: In this paper, a new robust approach based on a convolutional autoencoder using fuzzy clustering is proposed to address the cybersecurity and reliability issues in computer systems. Compared to existing methods, this approach is more efficient in feature extraction and handling outliers.
Article
Computer Science, Artificial Intelligence
Ali Mousavi, Richard G. Baraniuk
Summary: This article introduces a method called the uniform information coefficient (UIC), which is able to infer relationships among variables from large datasets. Compared to traditional methods, the UIC calculation is more efficient and robust to the type of association between variables.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Biochemical Research Methods
Emre Sefer
Summary: This paper presents BIOCODE, a framework for automatically discovering novel biological growth models that match user-specified graph attributes in directed and undirected biological graphs. BIOCODE combines basic instructions and a genetic algorithm optimization procedure to encode models for various biological networks. The framework has been evaluated on biological collaboration networks, gene regulatory networks, and protein interaction networks, successfully discovering models that closely match the true features of these networks.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Automation & Control Systems
Mohammad Sad Bashkari, Ashkan Sami, Mohammad Rastegar
Summary: By analyzing outage data and using association rule mining techniques, this article identifies the main factors causing power system outages and demonstrates the effectiveness and validity of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Ping Lou, Guantong Lu, Xuemei Jiang, Zheng Xiao, Jiwei Hu, Junwei Yan
Summary: This paper proposes a new method for detecting various intrusion behaviors in the cloud computing platform by mining association rules from multi-source logs. Experimental results show that the proposed method outperforms other algorithms in terms of calculation speed and performance metrics such as precision, recall, and f-measure.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Fuyuan Cao, Xiaolin Wu, Liqin Yu, Jiye Liang
Summary: This paper proposes an outlier detection algorithm for matrix-object data sets, which describes and calculates the outlier factor of matrix objects based on their coupling and cohesion. Experimental results have shown that the proposed algorithm effectively detects outliers compared to other algorithms on real and synthetic data sets.
APPLIED SOFT COMPUTING
(2021)
Article
Virology
Akash Sharma, Sweta Roy, Ruchika Sharma, Anoop Kumar
Summary: The study explores the association between antiviral drugs and DRESS syndrome and identifies potential signals using data mining algorithms and molecular docking studies. The selected antiviral drugs including abacavir, acyclovir, ganciclovir, lamivudine, lopinavir, nevirapine, ribavirin, ritonavir, and zidovudine are found to be associated with DRESS. Subgroup analysis reveals potential signals in different age groups and gender. The involvement of human leukocyte antigen (HLA)*B1502 and HLA*B5801 is indicated by the docking results. Acyclovir is the only antiviral drug that does not show a positive signal for DRESS.
JOURNAL OF MEDICAL VIROLOGY
(2023)
Article
Psychology, Multidisciplinary
Zhi Li, Xuyu Li, Runhua Tang, Lin Zhang
Summary: This study used the Apriori algorithm to mine strong rules related to global cyberspace security and found that professional websites had a higher focus on cyberspace security compared to non-professional websites. The core issues of global cyberspace security include internet sovereignty, cyber security, cyber attacks, cyber crime, and data protection.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Computer Science, Hardware & Architecture
Yuan Guo, Jingyong Zhou, Qiang Qin, Yun Wei, Weitang Zhang
Summary: With the deepening of AI theory research, pattern recognition has made further development and expanded its applicable fields. This article optimizes and analyzes intelligent manufacturing based on pattern recognition by using an improved association rule data mining algorithm. The experimental results show that the improved pattern recognition model significantly improves the efficiency and effectiveness of intelligent manufacturing.
IEEE CONSUMER ELECTRONICS MAGAZINE
(2023)
Article
Engineering, Multidisciplinary
Hongwei Du, Qiang Ye, Zhipeng Sun, Chuang Liu, Wen Xu
Summary: This study introduces two novel outlier detection algorithms for categorical data sets: Outlier Detection Tree (ODT) and FAST-ODT. ODT uses a classification tree and if-then rules to detect outliers in categorical data, while FAST-ODT achieves high detection accuracy with low time complexity.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Chao-Hua Yu
Summary: This paper presents a quantum algorithm called qARM for association rules mining, which achieves significant speedup over its classical counterpart. The algorithm is experimentally implemented on real quantum computers and a simulator, demonstrating its correctness and feasibility. The work serves as a benchmarking and provides prototypes for implementing qARM on larger transaction databases.
IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Byeol Kim, Benson Teck Heng Lim, Bee Lan Oo, Yong Han Ahn
Summary: This study aims to develop and validate a defect correlation evaluation model for project and property management professionals by examining the defect detection mechanisms of residential buildings and quantifying the mechanical characteristics of defects using association rules mining (ARM) techniques. The study proposes an ARM evaluation model that integrates and maps the classifications of building defects, addressing limitations of current evaluation approaches. The findings highlight the complex associations between defects and inform professionals of the most common occurrence defects, facilitating maintenance and repair planning.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)