Article
Automation & Control Systems
Bodrunnessa Badhon, Mir Md Jahangir Kabir, Md Asifur Rahman, Shuxiang Xu
Summary: The proposed new multi-objective reinforcement learning technique can optimize the applicability and coverage of membership functions, outperforming previous techniques in fuzzy association rule mining, and opening up new possibilities for future reinforcement learning endeavors.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Trinh T T Tran, Tu N Nguyen, Thuan T Nguyen, Giang L Nguyen, Chau N Truong
Summary: This paper proposes the NPSFF algorithm in fuzzy association rule mining, which combines the Node-list data structure and Pre-order Size Code structure to accelerate tree building and find frequent fuzzy item sets. By applying the AP clustering technique for data preprocessing and converting quantitative values to fuzzy values, the efficiency of the NPSFF algorithm is improved.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Jozsef Dombi, Tamas Jonas
Summary: This paper presents an operator-dependent, analytic membership function family derived from two soft inequalities using the middle hedge operator. This new membership function family is highly flexible and can be easily used in constructing a membership function system for fuzzy control systems.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
A. M. Garcia-Vico, C. J. Carmona, P. Gonzalez, M. J. del Jesus
Summary: This paper presents a distributed method based on evolutionary fuzzy systems for extracting and fusing patterns from data streams, and analyzes the adaptability and quality of the proposed method.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
S. Sharmila, S. Vijayarani
Summary: Association rule mining is a well-known data mining scheme used to discover commonly co-occurred itemsets, with frequent item recognition and association rule generation being key steps. Various algorithms have been developed by researchers to generate association rules, with fuzzy logic incorporated for uncovering recurrent itemsets and interesting fuzzy association rules. Dimensionality reduction techniques are proposed to effectively identify significant transactions and items from databases, while the efficiency of the proposed algorithm is compared with other optimization techniques for frequent item identification and rule generation.
Article
Computer Science, Artificial Intelligence
Onur Dogan, Furkan Can Kem, Basar Oztaysi
Summary: This study enhances the traditional association rule mining method by introducing fuzzy set theory to discover and display similar products in the e-commerce sector for customer selection. Experimental results show that the improved method provides decision-makers with abundant information on e-commerce sales.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Zohreh Anari, Abdolreza Hatamlou, Babak Anari
Summary: This paper explores the issue of mining fuzzy association rules in web data. It proposes a learning automata-based algorithm to optimize trapezoidal membership functions (TMF), improving mining efficiency.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Wenju Xu, Qingqing Zhao, Yu Zhan, Baocang Wang, Yupu Hu
Summary: Privacy protection during collaborative distributed association rule mining is an important research topic. In this study, the authors reviewed a privacy-preserving distributed association rule mining scheme proposed by Domadiya et al. for diagnosing heart disease in medical research. They found security vulnerabilities in the scheme and proposed a new scheme based on the distributed Paillier cryptosystem to address these shortcomings. Security analysis and performance evaluation demonstrated the efficiency and feasibility of the proposed scheme.
Review
Computer Science, Artificial Intelligence
Iztok Fister Jr, Iztok Fister, Dusan Fister, Vili Podgorelec, Sancho Salcedo-Sanz
Summary: Association rule mining aims to search for relationships between attributes in transaction databases. The process involves pre-processing techniques, rule mining, and post-processing with visualization. This review paper provides a literature review and analysis of techniques, applications, and future research directions in association rule mining and visualization.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ling Wang, Lingpeng Gui, Hui Zhu
Summary: A new algorithm called IFTARMFGT is proposed in this paper, combining the advantages of boolean matrix with incremental mining to mine fuzzy temporal association rules efficiently. Experiments show that this algorithm outperforms other algorithms in terms of efficiency and interpretability.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Lin Liu, Jinshu Su, Ximeng Liu, Rongmao Chen, Xinyi Huang, Guang Kou, Shaojing Fu
Summary: This paper investigates the privacy issues of frequent itemset mining and association rule mining on outsourced data in a two-cloud model, and proposes several secure computation protocols based on additively homomorphic cryptosystem and additive secret sharing. Experimental results show that our query scheme is more efficient than the existing work.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Mazen Sharaf AL-Deen, Lasheng Yu, Ali Aldhubri, Gamil R. S. Qaid
Summary: Recently, sentiment analysis research has gained popularity, allowing users to express their opinions on events, services, or products through social media applications. This paper proposes a sentiment classification method based on a fuzzy rule-based system and a crow search algorithm, achieving competitive performance according to experimental results.
Article
Nutrition & Dietetics
Yaqun Liu, Zhenxia Zhang, Wanling Lin, Hongxuan Liang, Min Lin, Junli Wang, Lianghui Chen, Peikui Yang, Mouquan Liu, Yuzhong Zheng
Summary: Food-components-target-function (FCTF) is an innovative exploration of interdisciplinary integration in the food field, based on association rule mining (ARM) and network interaction analysis. The FCTF model comprehensively explores the targets and functions using components as a basis, and is particularly suitable for preliminary studies of medicinal plants in remote and poor areas.
FRONTIERS IN NUTRITION
(2023)
Article
Computer Science, Artificial Intelligence
Z. Anari, A. Hatamlou, B. Anari
Summary: Association rule mining is an important technique in data mining for discovering relationships among data items. Finding appropriate membership functions is a challenge in fuzzy association rule mining. This study introduces a team of continuous action-set learning automata to determine the optimal number and positions of trapezoidal membership functions, and proposes a new approach to optimize these parameters. Experimental results show that the proposed algorithm improves the efficiency of rule extraction by finding optimized membership functions.
INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Theory & Methods
Ahmed Abu-Al Dahab, Riham M. Haggag, Samir Abu-Al Fotouh
Summary: The importance of CRM has increased, leading companies to adopt new strategies to focus on customers. Technological advancements have allowed companies to store customer data in large databases. Multilevel quantitative association mining is an important field for analyzing data components with multiple abstraction levels.
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2023)
Article
Geriatrics & Gerontology
Satoshi Noguchi, Michiko Makino, Satoru Haresaku, Kaoru Shimada, Toru Naito
GERIATRICS & GERONTOLOGY INTERNATIONAL
(2017)
Article
Dentistry, Oral Surgery & Medicine
Takashi S. Kajii, Takahiro Fujita, Yui Sakaguchi, Kaoru Shimada
CRANIO-THE JOURNAL OF CRANIOMANDIBULAR & SLEEP PRACTICE
(2019)
Article
Substance Abuse
Takashi Hanioka, Miki Ojima, Keiko Tanaka, Nao Taniguchi, Kaoru Shimada, Takeshi Watanabe
TOBACCO INDUCED DISEASES
(2018)
Article
Engineering, Electrical & Electronic
Shanqing Yu, Shingo Mabu, Manoj Kanta Mainali, Kaoru Shimada, Kotaro Hirasawa
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING
(2013)
Article
Engineering, Electrical & Electronic
Rong Zhang, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING
(2014)
Article
Dentistry, Oral Surgery & Medicine
Shinsuke Mizutani, Hisae Aoki, Satoru Haresaku, Kaoru Shimada, Michio Ueno, Keiko Kubota, Toru Naito
Article
Computer Science, Artificial Intelligence
Kaoru Shimada, Takaaki Arahira, Shogo Matsuno
Summary: In this paper, a method is proposed for discovering attribute combinations against a background of statistical characteristics. The method can directly find highly correlated attribute combinations from small populations and can be used for large-scale data analysis. Experimental results demonstrate the effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Shogo Matsuno, Kaoru Shimada
Summary: This study proposes a method to evaluate the settings of evolutionary operations in evolutionary rule discovery method. It focuses on the acquisition and accumulation of small results to solve the overall problem. By visualizing the progress and efficiency of problem solving, the difference in concept and evaluation of evolutionary computation during evolution is examined. The study obtains knowledge on setting up evolutionary operations for efficient rule-set discovery by introducing an index to visualize the efficiency of outcome accumulation.
PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Kaoru Shimada, Takaaki Arahira, Shogo Matsuno
Summary: This paper proposes a method for discovering attribute combinations directly in an incomplete database, as well as using evolutionary computations and association rules for large-scale data analysis, positioning it as an extension of statistical analysis methods.
2021 IEEE FOURTH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND KNOWLEDGE ENGINEERING (AIKE 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Kaoru Shimada, Takaaki Arahira, Shogo Matsuno
Summary: This paper proposes a rule discovery method that reveals attribute combinations of two consecutive variables of interest directly at high speed in numerical association rule mining. The method uses a strategy to pool solutions throughout generations and effectively finds narrow range attribute combinations for instance-based two-dimensional regression problems. Evaluation experiments show that the method is effective in discovering rules based on statistical distribution in NARM.
2021 IEEE 33RD INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Kaoru Shimada, Satoshi Noguchi, Michiko Makino, Toru Naito
MAN-MACHINE INTERACTIONS 5, ICMMI 2017
(2018)
Proceedings Paper
Computer Science, Cybernetics
Kaoru Shimada, Hisae Aoki, Keiko Kubota, Satoru Haresaku, Shinsuke Mizutani, Toru Naito, Michio Ueno
2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Kaoru Shimada, Takaaki Arahira, Takashi Hanioka
ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS, ICDM 2017
(2017)
Proceedings Paper
Computer Science, Software Engineering
Kaoru Shimada, Takaaki Arahira, Takashi Hanioka
2015 IEEE FIRST INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING SERVICE AND APPLICATIONS (BIGDATASERVICE 2015)
(2015)
Article
Computer Science, Artificial Intelligence
Kaoru Shimada, Takashi Hanioka
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS
(2015)