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Relevant bibliographies by topics / Numerical association rule mining

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Academic literature on the topic 'Numerical association rule mining'

Author: Grafiati

Published: 7 June 2025

Last updated: 26 June 2025

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Journal articles on the topic "Numerical association rule mining"

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Galvez, Tomida Akemi, and Andres Iglesias. "Online Numerical Association Rule Miner." Neurocomputing 528 (August 8, 2022): 33–43. https://doi.org/10.5281/zenodo.14263009.

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Mlakar, Uroš, Iztok Fister, and Iztok Fister. "NiaAutoARM: Automated Framework for Constructing and Evaluating Association Rule Mining Pipelines." Mathematics 13, no. 12 (2025): 1957. https://doi.org/10.3390/math13121957.

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Numerical Association Rule Mining (NARM), which simultaneously handles both numerical and categorical attributes, is a powerful approach for uncovering meaningful associations in heterogeneous datasets. However, designing effective NARM solutions is a complex task involving multiple sequential steps, such as data preprocessing, algorithm selection, hyper-parameter tuning, and the definition of rule quality metrics, which together form a complete processing pipeline. In this paper, we introduce NiaAutoARM, a novel Automated Machine Learning (AutoML) framework that leverages stochastic population-based metaheuristics to automatically construct full association rule mining pipelines. Extensive experimental evaluation on ten benchmark datasets demonstrated that NiaAutoARM consistently identifies high-quality pipelines, improving both rule accuracy and interpretability compared to baseline configurations. Furthermore, NiaAutoARM achieves superior or comparable performance to the state-of-the-art VARDE algorithm while offering greater flexibility and automation. These results highlight the framework’s practical value for automating NARM tasks, reducing the need for manual tuning, and enabling broader adoption of association rule mining in real-world applications.

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Hu, Qiwei, Shengbo Hu, and Mengxia Liu. "A Multi-Objective Nutcracker Optimization Algorithm Based on Cubic Chaotic Map for Numerical Association Rule Mining." Applied Sciences 15, no. 3 (2025): 1611. https://doi.org/10.3390/app15031611.

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Traditional numerical association rule mining optimization algorithms have limitations in handling discrete attributes, and they are susceptible to becoming trapped in local optima, uneven population distribution, and poor convergence. To address these challenges, we propose a multi-objective nutcracker optimization algorithm based on a cubic chaotic map (C-MONOA), specifically designed for mining association rules from mixed data (continuous and discrete). Unlike existing models, C-MONOA leverages a chaotic map for population initialization, alongside Michigan rule encoding, to dynamically optimize feature intervals during the optimization process. This algorithm integrates continuous and discrete data more effectively and efficiently. This article uses support, confidence, Kulc metric, and comprehensibility as evaluation indicators for multi-objective optimization. The experimental results show that C-MONOA performs well in rule scoring and can generate frequent, simple, and accurate rule sets. This study extends the association rule mining method for mixed data, demonstrating high performance and robustness and providing new technical tools for application fields such as market analysis and disease prediction.

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Watanabe, Toshihiko. "An Improvement of Fuzzy Association Rules Mining Algorithm Based on Redundancy of Rules." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (2011): 1248–55. http://dx.doi.org/10.20965/jaciii.2011.p1248.

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In data mining approach, quantitative attributes should be appropriately dealt with as well as Boolean attributes. This paper presents an essential improvement for extracting fuzzy association rules from a database. The objective of this paper is to improve the computational time of mining and to prune extracted redundant rules simultaneously for an actual data mining application. In this paper, we define the redundancy of fuzzy association rules as a new concept for mining and prove essential theorems concerning the redundancy of fuzzy association rules. Then, we propose a basic algorithm based on the Apriori algorithm for rule extraction utilizing the redundancy of the extracted rules. The essential performance of the algorithmis evaluated through numerical experiments using benchmark data. Fromthe results, themethod is found to be promising in terms of computational time and redundant-rule pruning.

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Liu, Xiaoyan, Feng Feng, Qian Wang, Ronald R. Yager, Hamido Fujita, and José Carlos R. Alcantud. "Mining Temporal Association Rules with Temporal Soft Sets." Journal of Mathematics 2021 (November 29, 2021): 1–17. http://dx.doi.org/10.1155/2021/7303720.

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Traditional association rule extraction may run into some difficulties due to ignoring the temporal aspect of the collected data. Particularly, it happens in many cases that some item sets are frequent during specific time periods, although they are not frequent in the whole data set. In this study, we make an effort to enhance conventional rule mining by introducing temporal soft sets. We define temporal granulation mappings to induce granular structures for temporal transaction data. Using this notion, we define temporal soft sets and their Q -clip soft sets to establish a novel framework for mining temporal association rules. A number of useful characterizations and results are obtained, including a necessary and sufficient condition for fast identification of strong temporal association rules. By combining temporal soft sets with NegNodeset-based frequent item set mining techniques, we develop the negFIN-based soft temporal association rule mining (negFIN-STARM) method to extract strong temporal association rules. Numerical experiments are conducted on commonly used data sets to show the feasibility of our approach. Moreover, comparative analysis demonstrates that the newly proposed method achieves higher execution efficiency than three well-known approaches in the literature.

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Stupan, Žiga, and Iztok Fister Jr. "NiaARM: A minimalistic framework for Numerical Association Rule Mining." Journal of Open Source Software 7, no. 77 (2022): 4448. http://dx.doi.org/10.21105/joss.04448.

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Jaramillo, Iván Fredy, Javier Garzás, and Andrés Redchuk. "Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm." Applied Sciences 11, no. 13 (2021): 6154. http://dx.doi.org/10.3390/app11136154.

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Association rule mining has been studied from various perspectives, all of which have made valuable contributions to data science. However, there are promising research lines, such as the inclusion of continuous variables and the combination of numerical and categorical attributes for a supervised classification variety. This research presents a new alternative for solving the numerical association rule-mining problem from an optimization perspective by using the VMO (Variable Mesh Optimization) meta-heuristic. This work includes the ability for classification when categorical data are available from a defined rule schema. Our technique implements an optimization process for the intervals of continuous variables, unlike others that discretize these types of variables. Some experiments were carried out with a real dataset to evaluate the quality of the rules obtained; in addition to this, this technique was compared with four population-based algorithms. The results show that this implementation is competitive in classification cases and has more satisfactory results for completely numerical data.

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Gakii, Consolata, Paul O. Mireji, and Richard Rimiru. "Graph Based Feature Selection for Reduction of Dimensionality in Next-Generation RNA Sequencing Datasets." Algorithms 15, no. 1 (2022): 21. http://dx.doi.org/10.3390/a15010021.

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Analysis of high-dimensional data, with more features (p) than observations (N) (p>N), places significant demand in cost and memory computational usage attributes. Feature selection can be used to reduce the dimensionality of the data. We used a graph-based approach, principal component analysis (PCA) and recursive feature elimination to select features for classification from RNAseq datasets from two lung cancer datasets. The selected features were discretized for association rule mining where support and lift were used to generate informative rules. Our results show that the graph-based feature selection improved the performance of sequential minimal optimization (SMO) and multilayer perceptron classifiers (MLP) in both datasets. In association rule mining, features selected using the graph-based approach outperformed the other two feature-selection techniques at a support of 0.5 and lift of 2. The non-redundant rules reflect the inherent relationships between features. Biological features are usually related to functions in living systems, a relationship that cannot be deduced by feature selection and classification alone. Therefore, the graph-based feature-selection approach combined with rule mining is a suitable way of selecting and finding associations between features in high-dimensional RNAseq data.

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Thanajiranthorn, Chartwut, and Panida Songram. "Efficient Rule Generation for Associative Classification." Algorithms 13, no. 11 (2020): 299. http://dx.doi.org/10.3390/a13110299.

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Associative classification (AC) is a mining technique that integrates classification and association rule mining to perform classification on unseen data instances. AC is one of the effective classification techniques that applies the generated rules to perform classification. In particular, the number of frequent ruleitems generated by AC is inherently designated by the degree of certain minimum supports. A low minimum support can potentially generate a large set of ruleitems. This can be one of the major drawbacks of AC when some of the ruleitems are not used in the classification stage, and thus (to reduce the rule-mapping time), they are required to be removed from the set. This pruning process can be a computational burden and massively consumes memory resources. In this paper, a new AC algorithm is proposed to directly discover a compact number of efficient rules for classification without the pruning process. A vertical data representation technique is implemented to avoid redundant rule generation and to reduce time used in the mining process. The experimental results show that the proposed algorithm archives in terms of accuracy a number of generated ruleitems, classifier building time, and memory consumption, especially when compared to the well-known algorithms, Classification-based Association (CBA), Classification based on Multiple Association Rules (CMAR), and Fast Associative Classification Algorithm (FACA).

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Agarwal, Reshu. "Opportunity Cost Estimation Using Clustering and Association Rule Mining." International Journal of Knowledge-Based Organizations 9, no. 4 (2019): 38–49. http://dx.doi.org/10.4018/ijkbo.2019100103.

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Information mining strategies are most appropriate for the classification, useful patterns extraction and predications which are imperative for business support and decision making. However, an efficient method for evaluating the penalty cost has not been proposed. In this article, considering the cross-selling effect, a quantitative approach to estimate the opportunity cost based on association rules in each cluster is proposed. This article helps in better decision making for improving sales, services and quality, which is useful mechanism for business support, investment, and surveillance. A numerical illustration is utilized to clarify the new approach. Further, to understand the effect of above approach in the real scenario, experiments are conducted on a real-world dataset.

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Dissertations / Theses on the topic "Numerical association rule mining"

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Wong, Wai-kit. "Security in association rule mining." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39558903.

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Wong, Wai-kit, and 王偉傑. "Security in association rule mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39558903.

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Palanisamy, Senthil Kumar. "Association rule based classification." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-131517/.

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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).

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Zhang, Ya Klein Cerry M. "Association rule mining in cooperative research." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6540.

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The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed January 26, 2010). Thesis advisor: Dr. Cerry M. Klein. Includes bibliographical references.

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Icev, Aleksandar. "DARM distance-based association rule mining." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0506103-132405.

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HajYasien, Ahmed. "Preserving Privacy in Association Rule Mining." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365286.

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With the development and penetration of data mining within different fields and disciplines, security and privacy concerns have emerged. Data mining technology which reveals patterns in large databases could compromise the information that an individual or an organization regards as private. The aim of privacy-preserving data mining is to find the right balance between maximizing analysis results (that are useful for the common good) and keeping the inferences that disclose private information about organizations or individuals at a minimum. In this thesis we present a new classification for privacy preserving data mining problems, we propose a new heuristic algorithm called the QIBC algorithm that improves the privacy of sensitive knowledge (as itemsets) by blocking more inference channels. We demonstrate the efficiency of the algorithm, we propose two techniques (item count and increasing cardinality) based on item-restriction that hide sensitive itemsets (and we perform experiments to compare the two techniques), we propose an efficient protocol that allows parties to share data in a private way with no restrictions and without loss of accuracy (and we demonstrate the efficiency of the protocol), and we review the literature of software engineering related to the associationrule mining domain and we suggest a list of considerations to achieve better privacy on software.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Information and Communication Technology<br>Faculty of Engineering and Information Technology<br>Full Text

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Vithal, Kadam Omkar. "Novel applications of Association Rule Mining- Data Stream Mining." AUT University, 2009. http://hdl.handle.net/10292/826.

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From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.

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Lin, Weiyang. "Association rule mining for collaborative recommender systems." Link to electronic version, 2000. http://www.wpi.edu/Pubs/ETD/Available/etd-0515100-145926.

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Rantzau, Ralf. "Extended concepts for association rule discovery." [S.l. : s.n.], 1997. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB8937694.

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Ahmed, Shakil. "Strategies for partitioning data in association rule mining." Thesis, University of Liverpool, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415661.

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Books on the topic "Numerical association rule mining"

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Zhang, Chengqi, and Shichao Zhang, eds. Association Rule Mining. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46027-6.

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Gkoulalas-Divanis, Aris, and Vassilios S. Verykios. Association Rule Hiding for Data Mining. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6569-1.

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Gkoulalas-Divanis, Aris. Association rule hiding for data mining. Springer, 2010.

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Dass, Rajanish. Classification using association rules. Indian Institute of Management, 2008.

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1978-, Koh Yun Sing, and Rountree Nathan 1974-, eds. Rare association rule mining and knowledge discovery: Technologies for infrequent and critical event detection. Information Science Reference, 2010.

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Kazienko, Przemysław. Associations: Discovery, analysis and applications. Oficyna Wydawnicza Politechniki Wrocławskiej, 2008.

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Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining. River Publishers, 2023.

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Amolochitis, Emmanouil. Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining. River Publishers, 2022.

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Amolochitis, Emmanouil. Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining. River Publishers, 2022.

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Amolochitis, Emmanouil. Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining. River Publishers, 2018.

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Book chapters on the topic "Numerical association rule mining"

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Kaushik, Minakshi, Rahul Sharma, Sijo Arakkal Peious, Mahtab Shahin, Sadok Ben Yahia, and Dirk Draheim. "On the Potential of Numerical Association Rule Mining." In Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4370-2_1.

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Fister, Iztok, Andres Iglesias, Akemi Galvez, and Iztok Fister. "Toward Reusing the Numerical Association Rule Mining Models." In 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87869-6_19.

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Fister, Iztok, Iztok Fister, Akemi Galvez, and Andres Iglesias. "TinyNARM: Simplifying Numerical Association Rule Mining for Running on Microcontrollers." In 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42529-5_12.

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Fister, Iztok, Andres Iglesias, Akemi Galvez, Javier Del Ser, Eneko Osaba, and Iztok Fister. "Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes." In Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03493-1_9.

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Tahyudin, Imam, and Hidetaka Nambo. "The Combination of Evolutionary Algorithm Method for Numerical Association Rule Mining Optimization." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1837-4_2.

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Li, Jiuyong, Hong Shen, and Rodney Topor. "An Adaptive Method of Numerical Attribute Merging for Quantitative Association Rule Mining." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-540-46652-9_5.

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Fister, Iztok, Vili Podgorelec, and Iztok Fister. "Improved Nature-Inspired Algorithms for Numeric Association Rule Mining." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68154-8_19.

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Tahyudin, Imam, and Hidetaka Nambo. "The Rules Determination of Numerical Association Rule Mining Optimization by Using Combination of PSO and Cauchy Distribution." In Proceedings of the Eleventh International Conference on Management Science and Engineering Management. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59280-0_12.

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Arakkal Peious, Sijo, Rahul Sharma, Minakshi Kaushik, Syed Attique Shah, and Sadok Ben Yahia. "Grand Reports: A Tool for Generalizing Association Rule Mining to Numeric Target Values." In Big Data Analytics and Knowledge Discovery. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59065-9_3.

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Pei, Bin, Fenmei Wang, and Xiuzhen Wang. "Mining Association Rules from a Dynamic Probabilistic Numerical Dataset Using Estimated-Frequent Uncertain-Itemsets." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52015-5_22.

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Conference papers on the topic "Numerical association rule mining"

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Tahyudin, Imam, and Hidetaka Nambo. "The rule extraction of numerical association rule mining using hybrid evolutionary algorithm." In 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2017. http://dx.doi.org/10.1109/eecsi.2017.8239202.

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Fister, Iztok, and Sancho Salcedo-Sanz. "Time Series Numerical Association Rule Mining for assisting Smart Agriculture." In 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2022. http://dx.doi.org/10.1109/icecet55527.2022.9873094.

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Kramberger, Renata, Iztok Fister, Iztok Fister, and Aida Kamišalić. "Toward Anomaly Detection in the Blockchain Using Numerical Association Rule Mining." In 2023 IEEE 21st Jubilee International Symposium on Intelligent Systems and Informatics (SISY). IEEE, 2023. http://dx.doi.org/10.1109/sisy60376.2023.10417913.

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Tan, Swee Chuan. "Improving Association Rule Mining Using Clustering-based Discretization of Numerical Data." In 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC). IEEE, 2018. http://dx.doi.org/10.1109/iconic.2018.8601291.

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Agbehadji, Israel Edem, Simon Fong, and Richard Millham. "Wolf search algorithm for numeric association rule mining." In 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). IEEE, 2016. http://dx.doi.org/10.1109/icccbda.2016.7529549.

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Saini, Pradeep Kumar, Divya Tomar, and Sonali Agarwal. "High numeric coherent association rule mining with a particular categorical consequent class attribute." In 2014 9th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2014. http://dx.doi.org/10.1109/iciinfs.2014.7036612.

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Zhang, Cheng-Lei, Jia-Jia Liu, Yi-Ning Zhang, and Kang Yang. "Research on Weighted Association Rules Mining Algorithm for the Medical Internet of Things Information Based on Fuzzy Numerical Constraints." In 2022 7th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2022. http://dx.doi.org/10.1109/icivc55077.2022.9887303.

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Fukuda, Takeshi, Yasuhido Morimoto, Shinichi Morishita, and Takeshi Tokuyama. "Mining optimized association rules for numeric attributes." In the fifteenth ACM SIGACT-SIGMOD-SIGART symposium. ACM Press, 1996. http://dx.doi.org/10.1145/237661.237708.

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Yang, Pu-Tai, Kai-Hao Yang, Ching-Chi Chen, and Shwu-Min Horng. "Subjective Association Rule Mining." In ICMLC 2018: 2018 10th International Conference on Machine Learning and Computing. ACM, 2018. http://dx.doi.org/10.1145/3195106.3195174.

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Hidber, Christian. "Online association rule mining." In the 1999 ACM SIGMOD international conference. ACM Press, 1999. http://dx.doi.org/10.1145/304182.304195.

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