Journal articles: '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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Song, Seung-Jae, Eung-Hee Kim, Hong-Gee Kim, and Harshit Kumar. "Query-based association rule mining supporting user perspective." Computing 93, no. 1 (2011): 1–25. http://dx.doi.org/10.1007/s00607-011-0148-x.

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Dash, Satya Ranjan, Satchidananda Dehuri, and Uma kant Sahoo. "Interactions and Applications of Fuzzy, Rough, and Soft Set in Data Mining." International Journal of Fuzzy System Applications 3, no. 3 (2013): 37–50. http://dx.doi.org/10.4018/ijfsa.2013070102.

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In this paper, interactions among fuzzy, rough, and soft set theory has been studied. The authors have examined these theories as a problem solving tool in association rule mining problems of data mining and knowledge discovery in databases. Although fuzzy and rough set have been well studied areas and successfully applied in association rule mining problem, but soft set theory needs more attention from both theoretical and practical side. Therefore, to make some improvement in this direction, the authors studied soft set theory and its interaction with fuzzy and rough set. Alongside, the authors have taken a numerical example related to a societal problem for realizing the practical importance of these theories.

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Agarwal, Reshu. "Modified Ranking With Temporal Association Rule Mining in Supply Chains." International Journal of Service Science, Management, Engineering, and Technology 11, no. 4 (2020): 58–71. http://dx.doi.org/10.4018/ijssmet.2020100104.

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This article deals with data mining applications for the supply chain inventory management. ABC classification is usually used for inventory items classification because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could affect the sale of other items (cross selling effect). Hence, within time-periods, the inventories should be classified. In this article, a modified approach is proposed considering both time-periods and cross-selling effect to rank inventory items. A numerical example and an empirical study with a data set are used to evaluate the proposed approach. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.

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Yin, Kuo-Cheng, Yu-Lung Hsieh, Don-Lin Yang, and Ming-Chuan Hung. "Association Rule Mining Considering Local Frequent Patterns with Temporal Intervals." Applied Mathematics & Information Sciences 8, no. 4 (2014): 1879–90. http://dx.doi.org/10.12785/amis/080446.

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Varol Altay, Elif, and Bilal Alatas. "Performance analysis of multi-objective artificial intelligence optimization algorithms in numerical association rule mining." Journal of Ambient Intelligence and Humanized Computing 11, no. 8 (2019): 3449–69. http://dx.doi.org/10.1007/s12652-019-01540-7.

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Can, Umit, and Bilal Alatas. "Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm." International Journal of Software Engineering and Knowledge Engineering 27, no. 03 (2017): 343–72. http://dx.doi.org/10.1142/s0218194017500127.

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The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

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Minaei-Bidgoli, B., R. Barmaki, and M. Nasiri. "Mining numerical association rules via multi-objective genetic algorithms." Information Sciences 233 (June 2013): 15–24. http://dx.doi.org/10.1016/j.ins.2013.01.028.

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Heraguemi, Kamel Eddine, Nadjet Kamel, and Habiba Drias. "Multi-objective bat algorithm for mining numerical association rules." International Journal of Bio-Inspired Computation 11, no. 4 (2018): 239. http://dx.doi.org/10.1504/ijbic.2018.092797.

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Drias, Habiba, Kamel Eddine Heraguemi, and Nadjet Kamel. "Multi-objective bat algorithm for mining numerical association rules." International Journal of Bio-Inspired Computation 11, no. 4 (2018): 239. http://dx.doi.org/10.1504/ijbic.2018.10013987.

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Tahyudin, Imam, and Hidetaka Nambo. "Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1359. http://dx.doi.org/10.11591/ijece.v9i2.pp1359-1373.

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>Particle Swarm Optimization (PSO) has been applied to solve optimization problems in various fields, such as Association Rule Mining (ARM) of numerical problems. However, PSO often becomes trapped in local optima. Consequently, the results do not represent the overall optimum solutions. To address this limitation, this study aims to combine PSO with the Cauchy distribution (PARCD), which is expected to increase the global optimal value of the expanded search space. Furthermore, this study uses multiple objective functions, i.e., support, confidence, comprehensibility, interestingness and amplitude. In addition, the proposed method was evaluated using benchmark datasets, such as the Quake, Basket ball, Body fat, Pollution, and Bolt datasets. Evaluation results were compared to the results obtained by previous studies. The results indicate that the overall values of the objective functions obtained using the proposed PARCD approach are satisfactory.</p></div></div></div>

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Kuo, R. J., Monalisa Gosumolo, and Ferani E. Zulvia. "Multi-objective particle swarm optimization algorithm using adaptive archive grid for numerical association rule mining." Neural Computing and Applications 31, no. 8 (2017): 3559–72. http://dx.doi.org/10.1007/s00521-017-3278-z.

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Tahyudin, Imam, and Hidetaka Nambo. "Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1359–73. https://doi.org/10.11591/ijece.v9i2.pp1359-1373.

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Particle Swarm Optimization (PSO) has been applied to solve optimization problems in various fields, such as Association Rule Mining (ARM) of numerical problems. However, PSO often becomes trapped in local optima. Consequently, the results do not represent the overall optimum solutions. To address this limitation, this study aims to combine PSO with the Cauchy distribution (PARCD), which is expected to increase the global optimal value of the expanded search space. Furthermore, this study uses multiple objective functions, i.e., support, confidence, comprehensibility, interestingness and amplitude. In addition, the proposed method was evaluated using benchmark datasets, such as the Quake, Basketball, Body fat, Pollution, and Bolt datasets. Evaluation results were compared to the results obtained by previous studies. The results indicate that the overall values of the objective functions obtained using the proposed PARCD approach are satisfactory.

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Lee, Suhwan, Marco Comuzzi, and Nahyun Kwon. "Exploring the Suitability of Rule-Based Classification to Provide Interpretability in Outcome-Based Process Predictive Monitoring." Algorithms 15, no. 6 (2022): 187. http://dx.doi.org/10.3390/a15060187.

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The development of models for process outcome prediction using event logs has evolved in the literature with a clear focus on performance improvement. In this paper, we take a different perspective, focusing on obtaining interpretable predictive models for outcome prediction. We propose to use association rule-based classification, which results in inherently interpretable classification models. Although association rule mining has been used with event logs for process model approximation and anomaly detection in the past, its application to an outcome-based predictive model is novel. Moreover, we propose two ways of visualising the rules obtained to increase the interpretability of the model. First, the rules composing a model can be visualised globally. Second, given a running case on which a prediction is made, the rules influencing the prediction for that particular case can be visualised locally. The experimental results on real world event logs show that in most cases the performance of the rule-based classifier (RIPPER) is close to the one of traditional machine learning approaches. We also show the application of the global and local visualisation methods to real world event logs.

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Dom Luís, Acácio, Rafael Benítez, and María del Carmen Bas. "Bridging Crisp-Set Qualitative Comparative Analysis and Association Rule Mining: A Formal and Computational Integration." Mathematics 13, no. 12 (2025): 1939. https://doi.org/10.3390/math13121939.

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In this paper, a novel mathematical formalization of Crisp-Set Qualitative Comparative Analysis (csQCA) that enables a rigorous connection with a specific class of association rule mining (ARM) problems is proposed. Although these two methodologies are frequently used to identify logical patterns in binary datasets, they originate from different traditions. While csQCA is rooted in set theory and Boolean logic and is primarily applied in the social sciences to model causal complexity, ARM originates from data mining and is widely used to discover frequent co-occurrences among items. In this study, we establish a formal mathematical equivalence between csQCA configurations and a subclass of association rules, including both positive and negative conditions. Moreover, we propose a minimization procedure for association rules that mirrors the Quine–McCluskey reduction method employed in csQCA. We demonstrate the consistency of the results obtained using both methodologies through two examples (a small-N study on internet shutdowns in Sub-Saharan Africa and a large-N analysis of immigration attitudes in Europe) and some numerical experiments. However, it is also clear that ARM offers improved scalability and robustness in high-dimensional contexts. Overall, these findings provide researchers with valuable theoretical and practical guidance when choosing between these approaches in qualitative data analysis.

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Zhang, Baoyi, Zhengwen Jiang, Yiru Chen, Nanwei Cheng, Umair Khan, and Jiqiu Deng. "Geochemical Association Rules of Elements Mined Using Clustered Events of Spatial Autocorrelation: A Case Study in the Chahanwusu River Area, Qinghai Province, China." Applied Sciences 12, no. 4 (2022): 2247. http://dx.doi.org/10.3390/app12042247.

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The spatial distribution of elements can be regarded as a numerical field of concentration values with a continuous spatial coverage. An active area of research is to discover geologically meaningful relationships among elements from their spatial distribution. To solve this problem, we proposed an association rule mining method based on clustered events of spatial autocorrelation and applied it to the polymetallic deposits of the Chahanwusu River area, Qinghai Province, China. The elemental data for stream sediments were first clustered into HH (high–high), LL (low–low), HL (high–low), and LH (low–high) groups by using local Moran’s I clustering map (LMIC). Then, the Apriori algorithm was used to mine the association rules among different elements in these clusters. More than 86% of the mined rule points are located within 1000 m of faults and near known ore occurrences and occur in the upper reaches of the stream and catchment areas. In addition, we found that the Middle Triassic granodiorite is enriched in sulfophile elements, e.g., Zn, Ag, and Cd, and the Early Permian granite quartz diorite (P1γδο) coexists with Cu and associated elements. Therefore, the proposed algorithm is an effective method for mining coexistence patterns of elements and provides an insight into their enrichment mechanisms.

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Ledmi, Makhlouf, Hamouma Moumen, Abderrahim Siam, Hichem Haouassi, and Nabil Azizi. "A Discrete Crow Search Algorithm for Mining Quantitative Association Rules." International Journal of Swarm Intelligence Research 12, no. 4 (2021): 101–24. http://dx.doi.org/10.4018/ijsir.2021100106.

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Association rules are the specific data mining methods aiming to discover explicit relations between the different attributes in a large dataset. However, in reality, several datasets may contain both numeric and categorical attributes. Recently, many meta-heuristic algorithms that mimic the nature are developed for solving continuous problems. This article proposes a new algorithm, DCSA-QAR, for mining quantitative association rules based on crow search algorithm (CSA). To accomplish this, new operators are defined to increase the ability to explore the searching space and ensure the transition from the continuous to the discrete version of CSA. Moreover, a new discretization algorithm is adopted for numerical attributes taking into account dependencies probably that exist between attributes. Finally, to evaluate the performance, DCSA-QAR is compared with particle swarm optimization and mono and multi-objective evolutionary approaches for mining association rules. The results obtained over real-world datasets show the outstanding performance of DCSA-QAR in terms of quality measures.

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Guo, Jia Mei, and Yin Xiang Pei. "Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm." Advanced Materials Research 998-999 (July 2014): 842–45. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.842.

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Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

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Han, Jianchao, Xiaohua Hu, and Nick Cercone. "A Visualization Model of Interactive Knowledge Discovery Systems and Its Implementations." Information Visualization 2, no. 2 (2003): 105–25. http://dx.doi.org/10.1057/palgrave.ivs.9500045.

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We briefly introduce an interactive visualization model, RuleViz, for knowledge discovery and data mining, which consists of five components: data preparation and visualization, interactive data reduction, data preprocessing, pattern discovery, and pattern visualization. With this model, the implementation issues are considered and three implementation paradigms, including image-based paradigm, algorithm-embedded paradigm, and interaction-driven paradigm, are discussed. We implement an interactive visualization system, AViz, which discovers 3D numerical association rules from large data sets based on the image-based paradigm. The framework of the AViz system is presented and each component is explored. To discretize numerical attributes, three approaches, including equal-sized, bin-packing-based equal-depth, and interaction-based approaches are proposed, and the algorithm for mining and visualizing numerical association rules is developed. Our experimental result on a census data set is illustrated, which shows that the AViz system is useful and helpful for discovering and visualizing numerical association rules.

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Varol Altay, Elif, and Bilal Alatas. "Intelligent optimization algorithms for the problem of mining numerical association rules." Physica A: Statistical Mechanics and its Applications 540 (February 2020): 123142. http://dx.doi.org/10.1016/j.physa.2019.123142.

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Altay, Elif Varol, and Bilal Alatas. "Differential evolution and sine cosine algorithm based novel hybrid multi-objective approaches for numerical association rule mining." Information Sciences 554 (April 2021): 198–221. http://dx.doi.org/10.1016/j.ins.2020.12.055.

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PAPADIMITRIOU, STERGIOS, SEFERINA MAVROUDI, and SPIRIDON D. LIKOTHANASSIS. "MUTUAL INFORMATION CLUSTERING FOR EFFICIENT MINING OF FUZZY ASSOCIATION RULES WITH APPLICATION TO GENE EXPRESSION DATA ANALYSIS." International Journal on Artificial Intelligence Tools 15, no. 02 (2006): 227–50. http://dx.doi.org/10.1142/s0218213006002643.

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Fuzzy association rules can reveal useful dependencies and interactions hidden in large gene expression data sets. However their derivation perplexes very difficult combinatorial problems that depend heavily on the size of these sets. The paper follows a divide and conquer approach to the problem that obtains computationally manageable solutions. Initially we cluster genes that more probably are associated. Thereafter, the fuzzy association rule extraction algorithms confront many but significantly reduced computationally problems that usually can be processed very fast. The clustering phase is accomplished by means of an approach based on mutual information (MI). This approach uses the mutual information as a similarity measure. However, the numerical evaluation of the MI is subtle. We experiment with the main methods and we compare between them. As the device that implements the mutual information clustering we use a SOM (Self-Organized Map) based approach that is capable of effectively incorporating supervised bias. After the mutual information clustering phase the fuzzy association rules are extracted locally on a per cluster basis. The paper presents an application of the techniques for mining the gene expression data. However, the presented techniques can easily be adapted and can be fruitful for intelligent exploration of any other similar data set as well.

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Hegland, Markus. "Data mining techniques." Acta Numerica 10 (May 2001): 313–55. http://dx.doi.org/10.1017/s0962492901000058.

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Methods for knowledge discovery in data bases (KDD) have been studied for more than a decade. New methods are required owing to the size and complexity of data collections in administration, business and science. They include procedures for data query and extraction, for data cleaning, data analysis, and methods of knowledge representation. The part of KDD dealing with the analysis of the data has been termed data mining. Common data mining tasks include the induction of association rules, the discovery of functional relationships (classification and regression) and the exploration of groups of similar data objects in clustering. This review provides a discussion of and pointers to efficient algorithms for the common data mining tasks in a mathematical framework. Because of the size and complexity of the data sets, efficient algorithms and often crude approximations play an important role.

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Alzamil, Zamil. "Exploring the Tradeoff between Utility and Privacy for Analyzing Stock Trend." Journal of Engineering and Applied Sciences 10, no. 2 (2023): 1. http://dx.doi.org/10.5455/jeas.2023110106.

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In this study of exploring the tradeoff between utility and privacy, we have used the S & P 500 data as a proof of concept, for Association Rule Data Mining using privacy-preserved data. The purpose of choosing S & P 500 was that unlike synthetically generated data, S & P 500 index though public, was real-world data nonetheless and hence incorporated in it all the political and social events that occurred over the period of time under consideration. Secondly, we could use stock price as a quasi-identifier. Any seasoned stockbroker would be able to identify the organization simply by looking at the stock price fluctuations over a period even if the ticker information is removed. And stock price is the most important variable used in any data mining related with stock market. Hence, we had to perturb this one variable in order to test for anonymity and utility. Finally, the data being numerical in nature helped us utilize the statistical data perturbation techniques. Association rule mining techniques were first applied to the original data. The data was then perturbed and, once again, using binary similarity algorithms, trend matching was performed on the identified stocks from the original data. The three tests of different random data generations were evaluated for data utility and privacy. The results re-emphasized on the tradeoff between the data utility and privacy where greater perturbation meant greater privacy but higher utility loss and vice versa.

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Soni, Sunita, and O. P. Vyas. "Building Weighted Associative Classifiers using Maximum Likelihood Estimation to Improve Prediction Accuracy in Health Care Data Mining." Journal of Information & Knowledge Management 12, no. 01 (2013): 1350008. http://dx.doi.org/10.1142/s0219649213500081.

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Associative classifiers are new classification approach that use association rules for classification. An important advantage of these classification systems is that, using association rule mining (ARM) they are able to examine several features at a time. Many applications can benefit from good classification model. Associative classifiers are especially fit to applications where the model may assist the domain experts in their decisions. Medical diagnosis is a domain where the maximum accuracy of the model is desired. In this paper, we propose a framework weighted associative classifier (WAC) that assigns different weights to different attributes according to their predicting capability. We are using maximum likelihood estimation (MLE) theory to calculate weight of each attribute using training data. We also show how existing Apriori algorithm can be modified in weighted environment to infer association rule from medical dataset having numeric valued attributes as the conventional ARM usually deals with the transaction database with categorical values. Experiments have been performed on benchmark data set to evaluate the performance of WAC in terms of accuracy, number of rules generating and impact of minimum support threshold on WAC outcomes. The result reveals that WAC is a promising alternative in medical prediction and certainly deserves further attention.

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Li, Yue, Francis A. Méndez-Mediavilla, Cecilia Temponi, Junwoo Kim, and Jesus A. Jimenez. "A Heuristic Storage Location Assignment Based on Frequent Itemset Classes to Improve Order Picking Operations." Applied Sciences 11, no. 4 (2021): 1839. http://dx.doi.org/10.3390/app11041839.

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Most large distribution centers’ order picking processes are highly labor-intensive. Increasing the efficiency of order picking allows these facilities to move higher volumes of products. The application of data mining in distribution centers has the capability of generating efficiency improvements, mainly if these techniques are used to analyze the large amount of data generated by orders received by distribution centers and determine correlations in ordering patterns. This paper proposes a heuristic method to optimize the order picking distance based on frequent itemset grouping and nonuniform product weights. The proposed heuristic uses association rule mining (ARM) to create families of products based on the similarities between the stock keeping units (SKUs). SKUs with higher similarities are located near the rest of the members of the family. This heuristic is applied to a numerical case using data obtained from a real distribution center in the food retail industry. The experiment results show that data mining-driven developed layouts can reduce the traveling distance required to pick orders.

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Subramanyam, R. B. V., and A. Goswami. "Mining Frequent Fuzzy Grids in Dynamic Databases with Weighted Transactions and Weighted Items." Journal of Information & Knowledge Management 05, no. 03 (2006): 243–57. http://dx.doi.org/10.1142/s0219649206001487.

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Incremental mining algorithms that derive the latest mining output by making use of previous mining results are attractive to business organisations. In this paper, a fuzzy data mining algorithm for incremental mining of frequent fuzzy grids from quantitative dynamic databases is proposed. It extends the traditional association rule problem by allowing a weight to be associated with each item in a transaction and with each transaction in a database to reflect the interest/intensity of items and transactions. It uses the information about fuzzy grids that are already mined from original database and avoids start-from-scratch process. In addition, we deal with "weights-of-significance" which are automatically regulated as the incremental databases are evolved and implant themselves in the original database. We maintain "hopeful fuzzy grids" and "frequent fuzzy grids" and our algorithm changes the status of the grids which have been discovered earlier so that they reflect the pattern drift in the updated quantitative databases. Our heuristic approach avoids maintaining many "hopeful fuzzy grids" at the initial level. The algorithm is illustrated with one numerical example and demonstration of experimental results are also incorporated.

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Beiranvand, Vahid, Mohamad Mobasher-Kashani, and Azuraliza Abu Bakar. "Multi-objective PSO algorithm for mining numerical association rules without a priori discretization." Expert Systems with Applications 41, no. 9 (2014): 4259–73. http://dx.doi.org/10.1016/j.eswa.2013.12.043.

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Panigrahi, Bhawani Sankar, Dr Sanjay Kumar, and Dr Pabitra Kumar Tripathy. "EOQ Model With Imperfect Items and Backorder with Allowable Proportionate Discount using Cross Selling Effects." International Journal of Innovative Technology and Exploring Engineering 12, no. 5 (2023): 18–24. http://dx.doi.org/10.35940/ijitee.e9485.0412523.

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The primary objective of this idea is to create an EOQ model for imperfectly-quality products that takes into account the combined effects of proportionate discount, backorder, clustering with association rule mining, and cross-selling. The ordering policy considering the cross-selling effect was first calculated in this paper. The benefits of cross-selling become especially evident when selling low-quality products. Data mining methods of varying sophistication are used to determine the nature of the connections between the objects. Clustering is used to group together items in the inventory database that are likely to be used together, and then the Apriori algorithm is used to build common item sets from inside each cluster. By the use of cross-selling, the most frequently purchased items are treated as unique entities. In addition, the EOQ of these entities is determined alongside the deficit threshold. Finally, a numerical example is taken into account to verify the results of the suggested work.

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Bhawani, Sankar Panigrahi, Sanjay Kumar Dr., and Pabitra Kumar Tripathy Dr. "EOQ Model With Imperfect Items and Backorder with Allowable Proportionate Discount using Cross Selling Effects." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 12, no. 5 (2023): 18–24. https://doi.org/10.35940/ijitee.E9485.0412523.

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<strong>Abstract: </strong>The primary objective of this idea is to create an EOQ model for imperfectly-quality products that takes into account the combined effects of proportionate discount, backorder, clustering with association rule mining, and cross-selling. The ordering policy considering the cross-selling effect was first calculated in this paper. The benefits of cross-selling become especially evident when selling low-quality products. Data mining methods of varying sophistication are used to determine the nature of the connections between the objects. Clustering is used to group together items in the inventory database that are likely to be used together, and then the Apriori algorithm is used to build common item sets from inside each cluster. By the use of cross-selling, the most frequently purchased items are treated as unique entities. In addition, the EOQ of these entities is determined alongside the deficit threshold. Finally, a numerical example is taken into account to verify the results of the suggested work.

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Yao, Chiyue, and Vinh Phuc Dung. "Analysis on the Establishment and Management of Library Resource Base Based on Modern Information Technology." Wireless Communications and Mobile Computing 2022 (August 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/5359767.

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The current rapid development of modern network information technology, along with the arrival of this trend, has profoundly changed people’s living and learning methods. In the past, the management of book resources in the traditional period has gradually exposed many drawbacks, and it is difficult to meet the technological development of the information age. Combined with the complexity of book management parameters, high variable dimensions, and the characteristics of multiattribute data points including not only numerical attributes but also category attributes and mixed attributes, the fuzzy clustering algorithm is combined with attribute weighted optimization, and then, the optimization is derived. Iterative formula and form show a weighted clustering algorithm to perform cluster analysis on relevant influencing factors in book data management. Digital library is not a library entity: it corresponds to various real social activities of public information management and dissemination and is manifested in various new information resource organizations and information dissemination services. As a fuzzy association rule mining algorithm, FP-growth algorithm is obviously better than Apriori algorithm in execution efficiency. However, due to the lack of fuzzy attributes and the large space complexity, the FP-growth algorithm cannot achieve effective multilayer association rule mining when dealing with large transaction databases, such as library databases. First, the algorithm divides the large-scale book transaction database into several sub-databases according to the transaction of the first item and constructs the corresponding sub-FP-tree structure; then, the parent items that are not frequent items in the hierarchical tree are filtered out in real time to reduce the scanning space.

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Anzari, Fajar, Winnie Septiani, Dedy Sugiarto, and Martino Luis. "A fault diagnosis system for CNC hydraulic machines: a conceptual framework." SINERGI 27, no. 1 (2023): 65. http://dx.doi.org/10.22441/sinergi.2023.1.008.

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The fault diagnosis process in Computer Numerical Control (CNC) hydraulic machines for steel processing relies on skills, experiences, and maintenance technicians' understanding of the machine. The problem is many junior maintenance technicians are inexperienced and unskilled. This paper proposes a conceptual framework for a fault diagnosis system for the CNC hydraulic machine to help a maintenance technician in a fault diagnosis process. The framework uses association rule mining to discover hidden association patterns between fault symptoms and causes from historical machine fault data. The framework has consisted of data standardization, knowledge acquisition, and a model of the fault diagnosis system. The data standardization aims to make the data ready to be mined by assigning a fault tag for each record of historical fault data. The tagged repair records are used to produce symptoms–cause associative knowledge. The produced knowledge is refined by corrective actions acquired from expert knowledge. The knowledge is then stored in the fault knowledge database in the form of IF-THEN rules. The reasoning machine is developed to map the fault symptoms as IF and the causes as THEN. Production operators can fill in the fault symptoms by choosing the standardized fault symptom tag. When a maintenance technician reviews a fault report, the system, through a reasoning machine, will access the appropriate IF-THEN rules based on the fault symptoms that the production operator has filled in. The system concludes the fault cause and recommends suitable corrective action.

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Bozhenko, Andrii, Dariusz Krawczyk, Karolina Hałuszko, and Valerii Ozarenko. "Data-Mining Modeling of Corruption Perception Patterns Based on Association Rules." Business Ethics and Leadership 7, no. 4 (2023): 181–89. http://dx.doi.org/10.61093/bel.7(4).181-189.2023.

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Corruption is one of the many challenges facing Ukrainian society. After the full-scale war, 89% of Ukrainian citizens consider corruption to be the most serious problem for the stable development of Ukraine. In the article, it is argued that neglecting the problem of corruption in society leads to a decrease in the level of trust in public authorities, economic losses, a decrease in the country’s investment attractiveness, a loss of trust from international partners, as well as other economic and political turbulences. The study aims to develop an economic and mathematical model for determining the patterns of corruption perception in Ukraine based on association rules implemented using data mining methods. The Python Numerical Python library was used to build association rules. The study of the patterns of corruption perception was analysed in terms of the legislative (based on the activities of the Verkhovna Rada of Ukraine), judicial (based on the activities of the courts) and executive (on the example of the activities of the President and his Office) branches of government. The source of primary data was the data of a survey conducted by the “Join!” Public Participation Support Program together with the USAID project “Support for Leader Organizations in Combating Corruption in Ukraine”. 38 association rules were determined for the judicial system, among which the most informative consequences were “distrust in the judicial system” and “corruption is widespread in the courts”. For the executive branch of government, 27 association rules were built, where the informative consequence was “citizens can influence, namely participate in public councils or public hearings to monitor state institutions and their decisions”. For the legislative branch, 33 association rules were formed, where the informative consequence was “citizens can influence, namely participate in public councils or public hearings to monitor state institutions and their decisions”. According to the empirical calculations, a regularity was found that Ukrainian society is democratic and believes in its power to overcome corruption in any branch of government, but members of society do not show active desire and only revolutionary manifestations are possible in a critical situation. The Verkhovna Rada of Ukraine can use the results obtained during the improvement of Ukrainian legislation, the Cabinet of Ministers of Ukraine when approving the State Program for the Implementation of the Anti-Corruption Strategy and other state bodies in developing anti-corruption programs at various levels.

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Patacsil, Frederick F., Jennifer M. Parrone, Monica B. Brosas, and Bobby F. Roaring. "Analysis of Concerns of the Agricultural Sector in the Philippines using Associative Rule." International Journal of Membrane Science and Technology 10, no. 3 (2023): 3316–24. http://dx.doi.org/10.15379/ijmst.v10i3.3293.

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The Philippines is an agricultural country famous for its wide range of natural resources scattered over the archipelago. The country’s agriculture sector comprises four sub-sectors: fisheries, farming, livestock, and forestry. The COVID-19 pandemic, according to the Food and Agriculture Organization, is straining food systems and causing food insecurity across the world. This study used a mixed method that analyzed qualitative data using quantitative analysis. Concerns voiced by agricultural sectors were analyzed utilizing the frequencies of words used. The study utilized the frequency of the words was TF-IDF or Term Frequency – Inverse Document Frequency to analyze agricultural word concerns. This schema was used as a numeric measure to show the importance of using words to voice out their concerns. The next step is the determination of word patterns using association rule Association Rule Mining. The result reveals the prevalent words used to express concern by the agriculture sector are "cash" and "seed" and "assist" + "cash" is the most frequent word pattern. The word "cash," the most commonly used word used by the agriculture sector to air their concern, reveals that this sector needs cash assistance to finance their agricultural activities. The result of this study can be utilized to address concerns in the agriculture sector. Furthermore, this research can be utilized in other sectors to analyze their concerns and provide necessary interventions.

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Ahyuna, A., Marlin Lasena, Rosihan Aminuddin, Ardimansyah Ardimansyah, and Zulfi Azhar. "Pembentukan Pola Peminjaman Buku Pada Perpustakaan Dengan Menerapkan Metode CART dan Normalisasi Z-Score." Building of Informatics, Technology and Science (BITS) 6, no. 1 (2024): 314–24. https://doi.org/10.47065/bits.v6i1.5238.

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The library is a service place that is useful as a place to borrow all kinds of books to read or as reference material in the form of images, text or other forms. In simplifying book borrowing strategies, the library utilizes realistic borrowing data as an object for strategy discovery by exploring knowledge that can provide information to simplify the book borrowing system. Data Mining can be interpreted as data mining, where in the data mining process a data mining process is carried out which aims to find important, valuable and useful information in a very large collection of databases. If the data held is very large, it is necessary to carry out a preliminary process as a stage to help simplify the process carried out in data mining, or this process is known as data normalization. One method that can be used in the process of normalizing data is the Z-Score Normalization method. The Z-Score Normalization method is a process in the preprocessing stage by decomposing numerical attribute data which can change the values in the data into a certain range. The Z-Score Normalization method itself can also be combined with other methods such as the CART method. The CART method is a method used to carry out the classification process in data mining. The classification process carried out using the CART method is based on the formation of a decision tree with the binary values obtained. The CART method itself is a method used to assist in processing all types of data such as continuous, ordinal, nominal and other data. The results obtained from the Cart Method research can be applied to obtain book borrowing patterns or book association relationships in libraries in the form of rules which can provide important information about book borrowing patterns in the library

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Gutierrez-Rojas, Daniel, Ioannis T. Christou, Daniel Dantas, Arun Narayanan, Pedro H. J. Nardelli, and Yongheng Yang. "Performance evaluation of machine learning for fault selection in power transmission lines." Knowledge and Information Systems 64, no. 3 (2022): 859–83. http://dx.doi.org/10.1007/s10115-022-01657-w.

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AbstractLearning methods have been increasingly used in power engineering to perform various tasks. In this paper, a fault selection procedure in double-circuit transmission lines employing different learning methods is accordingly proposed. In the proposed procedure, the discrete Fourier transform (DFT) is used to pre-process raw data from the transmission line before it is fed into the learning algorithm, which will detect and classify any fault based on a training period. The performance of different machine learning algorithms is then numerically compared through simulations. The comparison indicates that an artificial neural network (ANN) achieves remarkable accuracy of 98.47%. As a drawback, the ANN method cannot provide explainable results and is also not robust against noisy measurements. Subsequently, it is demonstrated that explainable results can be obtained with high accuracy by using rule-based learners such as the recently developed quantitative association rule mining algorithm (QARMA). The QARMA algorithm outperforms other explainable schemes, while attaining an accuracy of 98%. Besides, it was shown that QARMA leads to a very high accuracy of 97% for highly noisy data. The proposed method was also validated using data from an actual transmission line fault. In summary, the proposed two-step procedure using the DFT combined with either deep learning or rule-based algorithms can accurately and successfully perform fault selection tasks but indicating remarkable advantages of the QARMA due to its explainability and robustness against noise. Those aspects are extremely important if machine learning and other data-driven methods are to be employed in critical engineering applications.

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Gonen, Yaron, Ehud Gudes, and Kirill Kandalov. "New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework." Algorithms 11, no. 12 (2018): 194. http://dx.doi.org/10.3390/a11120194.

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The Map-Reduce (MR) framework has become a popular framework for developing new parallel algorithms for Big Data. Efficient algorithms for data mining of big data and distributed databases has become an important problem. In this paper we focus on algorithms producing association rules and frequent itemsets. After reviewing the most recent algorithms that perform this task within the MR framework, we present two new algorithms: one algorithm for producing closed frequent itemsets, and the second one for producing frequent itemsets when the database is updated and new data is added to the old database. Both algorithms include novel optimizations which are suitable to the MR framework, as well as to other parallel architectures. A detailed experimental evaluation shows the effectiveness and advantages of the algorithms over existing methods when it comes to large distributed databases.

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Yan Hai, and Zhang Tietou. "Association Rules Mining based on Numeric Constraint." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 23 (2012): 634–42. http://dx.doi.org/10.4156/aiss.vol4.issue23.78.

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Fukuda, Takeshi, Yasuhiko Morimoto, Shinichi Morishita, and Takeshi Tokuyama. "Mining Optimized Association Rules for Numeric Attributes." Journal of Computer and System Sciences 58, no. 1 (1999): 1–12. http://dx.doi.org/10.1006/jcss.1998.1595.

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Lekha, A., C. V. Srikrishna, and Viji Vinod. "Fuzzy Association Rule Mining." Journal of Computer Science 11, no. 1 (2015): 71–74. http://dx.doi.org/10.3844/jcssp.2015.71.74.

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Hidber, Christian. "Online association rule mining." ACM SIGMOD Record 28, no. 2 (1999): 145–56. http://dx.doi.org/10.1145/304181.304195.

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Journal articles on the topic 'Numerical association rule mining'

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