Mining Algorithms Research Articles

A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System

AimsThe primary objective of this study was to closely monitor and identify adverse events (AEs) associated with Sorafenib, a pharmacological therapeutic agent used to treat hepatocellular carcinoma, renal cell carcinoma, and thyroid cancer. The ultimate goal was to optimize patient safety and provide evidence-based guidance for the appropriate use of this drug.MethodsReports from the FDA Adverse Event Reporting System (FAERS) database were comprehensively collected and analyzed, covering the first quarter of 2004 to the first quarter of 2024. Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.ResultsIn total, we identifued 18,624 patients (82,857 AEs in the Sorafenib population) from the collected reports and examined, the occurrence of Sorafenib-induced AEs in 26 organ systems. The study results revealed the presence of the expected AEs, including Diarrhoea, Palmar-plantar erythrodysaesthesia syndrome, Hepatocellular carcinoma, Fatigue, and Rash, which was consistent with the information provided in the drug insert. In addition, unexpected significant AEs, such as Gait inability, Palmoplantar keratoderma and Hyperkeratosis were observed at the preferred term (PT) level. These findings suggest the potential occurrence of adverse reactions not currently documented in drug descriptions.ConclusionThis study successfully detected new and unforeseen signals associated with Sorafenib-related AEs related to Sorafenib administration, providing important insights into the complex correlations between AEs and Sorafenib use. The results of this study emphasize the critical importance of continuous and vigilant surveillance for the timely identification and effective management of AEs to improve the overall patient safety and wellbeing in the context of Sorafenib therapy.

Optimisasi Penilaian Kinerja Karyawan PT. Tolan Tiga Indonesia Estate Perlabian Dengan Algoritma C4.5

In the face of fierce competition in the current global era, companies are required to prepare and always adjust their strategies to the changes that occur so that the company remains able to compete and survive. Employees who are in a company are workers which is the most important asset that must be owned and indispensable in the company and of course must be considered by all parties in order to create good performance as well as have goals to be achieved in the assessment of employee performance at PT. There Are Three Types Of Indonesian Real Estate. Employee performance appraisal in the company is seen as the driving force of the company because human resources play an active role in the running of an organization or company and the decision-making process. Machine learning tools used in predicting the assessment, using the C4.5 algorithm, the data obtained is more accurate. Machine learning is an artificial intelligence that can process data that is useful for consideration in making decisions and solving problems. C4.5 algorithm is one of the algorithms in data mining that serves to classify a class. This algorithm is a development of the ID3 algorithm. How the C4.5 algorithm works by forming a decision tree to produce a decision.

DIAFM: An Improved and Novel Approach for Incremental Frequent Itemset Mining

Traditional approaches to data mining are generally designed for small, centralized, and static datasets. However, when a dataset grows at an enormous rate, the algorithms become infeasible in terms of huge consumption of computational and I/O resources. Frequent itemset mining (FIM) is one of the key algorithms in data mining and finds applications in a variety of domains; however, traditional algorithms do face problems in efficiently processing large and dynamic datasets. This research introduces a distributed incremental approximation frequent itemset mining (DIAFM) algorithm that tackles the mentioned challenges using shard-based approximation within the MapReduce framework. DIAFM minimizes the computational overhead of a program by reducing dataset scans, bypassing exact support checks, and incorporating shard-level error thresholds for an appropriate trade-off between efficiency and accuracy. Extensive experiments have demonstrated that DIAFM reduces runtime by 40–60% compared to traditional methods with losses in accuracy within 1–5%, even for datasets over 500,000 transactions. Its incremental nature ensures that new data increments are handled efficiently without needing to reprocess the entire dataset, making it particularly suitable for real-time, large-scale applications such as transaction analysis and IoT data streams. These results demonstrate the scalability, robustness, and practical applicability of DIAFM and establish it as a competitive and efficient solution for mining frequent itemsets in distributed, dynamic environments.

Effectiveness of the Implementations of a Data Mining Algorithm Based Peak Restriction Method

The rapid growth of data in various domains has necessitated the development of advanced algorithms to efficiently analyze and manage large datasets. One of the key challenges in data mining is dealing with peak values or outliers, which can distort the analysis and lead to inaccurate predictions or decisions. This paper investigates the effectiveness of a data mining-based peak restriction method designed to minimize the influence of extreme values in large datasets. The proposed method uses a combination of data preprocessing techniques and machine learning algorithms to identify and limit peak values, enhancing the robustness and accuracy of predictive models. We evaluate the method across different datasets, including time-series, transactional, and sensor data, to assess its generalizability and performance. The results demonstrate significant improvements in model accuracy and stability, especially in applications sensitive to outliers, such as anomaly detection and predictive maintenance. Our findings suggest that the peak restriction method is a promising tool for improving the quality of data-driven models, with potential applications in various industries including finance, healthcare, and IoT. Furthermore, we discuss the limitations of the approach and propose future directions for further optimization.

A Scalable Framework for Secure and Reliable Wireless-based Fog Cloud Communication

—Wireless telecommunication systems are essential in transferring data through fog cloud servers. However, the fog cloud servers suffer from unreliable and non-secure routing and limited power resources when mobile users are mobile. This paper introduces a scalable framework (SFRRDC) for reliable wireless routing and secure data transfer. We aim to address security and reliability issues by combining pheromone termite (PT) characteristics and ant colony optimization (ACO) algorithms for high-performance, more secure, highly reliable data transfer among the fog cloud servers. For that, the proposed SFRRDC supports a user authentication mining (UAM) algorithm to secure the confidentiality of the users. Based on testing results, it is confirmed that the proposed SFRRDC provides a better solution for confidentiality and fast routing for a wireless telecommunication system. The results show that the proposed SFRRDC framework’s energy consumption outperforms competing frameworks’ energy consumption with a better-achieved latency. For example, the suggested SFRRDC method uses 748.5 Joules during 72 hours of operation, competing frameworks use more energy, and DDFQFR uses 976.22 Joules. It also shows that the proposed SFRRDC is immune to malicious attacks. The results show that with 3,600 Fog cloud users, the proposed HEE protocol reduces the number of attacks to only 48 compared to 58, 72, and 77415 expected malicious attacks when using the ICDRP-F-SDVN, ACO, and DDFQFR frameworks, respectively.

Assessment of phenotypic diversity and morphometry of indigenous chickens in Kabwe District, Zambia

This study characterized Zambian indigenous chickens' phenotypes and morphometric traits in Kabwe District, Zambia, to create a base for breeding program development. To achieve this, 206 hens and 228 cocks we sampled in Kabwe district. Each sampled chicken was phenotyped for 4 qualitative traits: ear lobe color, shank color, comb type, and eye color. Individual linear body measurements were recorded from each chicken using a tailor's tape in centimeters, which included body length (BL), chest circumference (CC), corpus length (CL), keel length (KL), shank circumference (SC), shank length (SL), thigh circumference (TC), and thigh length (TL). The findings showed that the predominant phenotypes were single comb type (98.91%), orange eyes (80.44%), white shanks (33.70%), and red-white earlobe color (50%). The average body weight was 1698.24 g, which showed a significant positive correlation (p<0.05) with all linear body measurements. For both sexes, body weight correlated positively with all linear measurements, except in males where body length showed a nonsignificant negative correlation. A stepwise regression analysis indicated that chest circumference, keel length, body length, shank circumference, thigh circumference, and corpus length (CL) are significant predictors of body weight (R² = 76.90%, P < 0.05). Additionally, the CHAID data mining algorithm identified keel length as the primary predictor of body weight, with corpus length and thigh length also contributing, achieving an R² of 61.20%. These findings provide valuable insights for instituting chicken breeding programs aimed at enhancing productivity and selection decisions.

Space-time tree: a spatiotemporal construct for efficient similarity matrix calculations among network-constrained trajectories

Data mining of network-constrained trajectories has broad applications in the GIScience field. The calculation of a complete trajectory similarity matrix is a key step in various data mining algorithms. However, computing this matrix is computationally intensive for large datasets, as it involves numerous point-to-point shortest-path (PPSP) queries. To tackle this issue, we propose a new spatiotemporal construct called the space-time tree, which directly delineates the network distance from a query trajectory to any network space-time point. By constructing the space-time tree, we can efficiently compute the trajectory similarity matrix without additional PPSP queries. The space-time tree supports several similarity metrics, including closest pair distance, furthest pair distance, longest common subsequence (LCSS), and distance-weighted LCSS. It can further integrate with advanced spatiotemporal query techniques for scalable partial trajectory similarity matrix calculations. A case study using real datasets was conducted to apply the space-time tree in the trajectory clustering application. The results show that the space-time tree completed the clustering task on 0.5 million trajectories within 49 minutes, achieving a nearly 147-fold speedup compared to state-of-the-art methods.

Towards the Implementation of Heuristics Miner in .Net Framework

Process mining is an interdisciplinary field that bridges the gap between data mining and business process analysis. It revolves around the core concept of leveraging recorded event logs to automate the generation, validation, and refinement of process model. A majority of currently accessible process mining solutions are distributed in form of black box systems - software with no openly accessible source code - or as libraries for scripting languages like Python or R. In this sense, it is necessary to design and build an understandable foundation of a process mining library with the intention of presenting the possibility of incorporating process mining into many programs written in the language or aiding other programmers interested in the potential of such technologies. Therefore, this paper describes and demonstrates the usability of an advanced algorithm for process mining in the .NET platform, namely, the Heuristic miner for process modelling.

An Accurate Construction Method of E-commerce User Profile Based on Artificial Intelligence Algorithm and Big Data Analysis

User’s basic attributes, behavior characteristics, value attributes, social attributes, interest attributes, psychological attributes, and other factors will lead to poor user experience, information overload, interference, and other negative effects. In order to develop more accurate marketing strategies, optimize user experience, and improve the conversion rate and user satisfaction of e-commerce platforms, an accurate construction method of e-commerce user profile based on artificial intelligence algorithm and big data analysis is proposed. Based on big data analysis technology, the basic attributes, behavior characteristics, value attributes, social attributes, interest attributes, and psychological attributes of e-commerce users are collected and integrated from multiple dimensions. The improved sequential pattern mining algorithm (PBWL) is applied to mine the frequent sequential pattern in the e-commerce user behavior, and to reveal the user’s behavior habit. The comprehensive attribute representation of e-commerce users is obtained by combining the LINE network model and the convolutional neural network. The firefly K-means clustering algorithm is used to cluster the e-commerce users, group the users based on the similarity of user attribute information, create different types of user clusters, and achieve the accurate construction of an e-commerce user profile. The experimental results show that this method can build an accurate e-commerce user profile and provide strong support for personalized recommendation and precision marketing of e-commerce platforms. This method can dig deeply into the behavior habits of e-commerce users and accurately reflect their interest preferences and consumption characteristics. This method can quickly and stably cluster e-commerce users, and the clustering effect of user profiles is optimal. This method can also divide the data into meaningful groups according to the user’s consumption behavior, and reveal the characteristics and values of different groups.

A deep learning-based intelligent method for mining sentiment words in english translation texts

Abstract The current conventional sentiment lexicon mining methods mainly achieve lexical extraction by mining the sentiment features of words, which leads to poor mining effect due to the lack of sentiment lexicon construction. In this regard, the intelligent mining method of sentiment lexicon of English translation text based on deep learning is proposed. The intelligent mining algorithm is optimized by extracting lexical templates with generality, constructing sentiment lexicon, and building convolutional network to extract lexical features. The experimental results showed that this mining method had an accuracy of over 85% in mining emotional vocabulary in translated texts, which was higher than other algorithms. Meanwhile, the mining time required for this algorithm was not yet 4.5 min. The above results indicated that the proposed method had higher accuracy and faster speed in mining emotional vocabulary in English translation texts.

Investigation of factors affecting fresh herbage yield in pea (Pisum arvense L.) using data mining algorithms.

This study was carried out to determine the factors affecting the wet grass yield of pea plants grown in Turkey. Wet grass yield was predicted using parameters such as genotype, crude protein, crude ash, acid detergent fiber (ADF), and neutral detergent fiber (NDF) with some data mining algorithms. These techniques provided easily interpretable data trees and precise cutoff values. This led to a comparison of the predictive abilities of data mining methods, including multivariate adaptive regression spline (MARS), Chi-square automatic interaction detection (CHAID), classification and regression tree (CART), and artificial neural network (ANN). To test the compatibility of the data mining algorithms, seven goodness-of-fit criteria were used. The predictive abilities of the fitted models were assessed using model fit statistics such as the coefficient of determination (R 2), adjusted R 2, root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), Akaike information criterion (AIC), and corrected Akaike information criterion (AICc). With the greatest R 2 and adjusted R 2 values (0.998 and 0.986) and the lowest values of RMSE, MAPE, SD ratio, AIC, and AICc (10.499, 0.7365, 0.047, 268, and 688, respectively), the MARS method was determined to be the best model for quantifying plant fresh herbage yield. In estimating the fresh herbage production of the pea plant, the results showed that the MARS method was the most appropriate model and a good substitute for other data mining techniques.

Analysis of Economic Development Mechanism Based on Stochastic Dynamic Optimization Algorithm

With the continuous improvement of China’s economic level, the research of economic data management has been paid more and more attention. As the high-tech industry plays an increasingly important role in the world economy, it has become a strategic highland for all countries in the world. How to design an economic data management model that integrates data mining techniques to achieve deep data mining and efficient utilization has become a research focus. In order to study the technical selection model of economic data, a new economic data management model based on a data mining algorithm is proposed. In the study of economic management data, the optimization model of data management is designed based on computer technology. In the optimization of data classification, a data mining algorithm is adopted to fully realize the reasonable management of data. According to the principle of the Random Dynamic Optimization Algorithm, the algorithm is improved, and its convergence speed is accelerated. The improved algorithm is applied to solve the one-dimensional cutting problem. Compared with other algorithms, the conversion method greatly improves the utilization rate of raw materials. Then, the efficiency and adaptability of the algorithm are tested by determining the algorithm and applying it to the knapsack problem.

Optimizing Procurement Efficiency by Implementing K-Means and Random Forest in Kopegtel Samarinda’s Warehouse System

Procurement is a company’s activity to purchase goods or equipment needed in operations. In the management process, a procurement management system is often used to facilitate this management, such as at CV Indocitra Multi Artha, which uses the “Sistem Warehouse Kopegtel Samarinda.” The system provides significant assistance to the company, but large requests can be overwhelming to be handled by the manager and can cause an overload information problem. Research was conducted to deal with these problems by implementing a data mining algorithm as a procurement recommendation system. K-means and Random Forest algorithms were chosen as methods for the research. The algorithm is processed within two critical steps, first by K-Means to get cluster data and then by predicting it with Random Forest to get a recommendation for whether the object should be bought or not. Hyperparameter tuning was performed to optimize the model’s performance, yielding an F1-Score of 86.95%, representing the balance between precision and recall, and an ROC AUC value of 82.34%. These substantial metric outcomes indicate that the model can provide practical recommendations

Predicting Students’ Academic Performance Using Data Mining Method

Predicting students' academic performance is a critical task in educational institutions to identify students likely to succeed or need intervention. This study leverages data mining techniques to analyze and predict student outcomes, contributing to more informed decision-making in educational management. Using data from various academic sources, the research implements three popular data mining algorithms—Naive Bayes, Multilayer Perceptron, and C4.5 Decision Tree—to classify student success. These techniques allow the identification of patterns and dependencies in student data, providing insights that can assist educators in creating tailored interventions. The results indicate that the Naive Bayes algorithm outperforms other classifiers in terms of prediction accuracy, making it a viable tool for predicting academic performance. This research underscores the potential of data mining to enhance educational outcomes by allowing proactive responses to students' academic needs.

Bi-objective Optimization in Role Mining

Role mining is a technique that is used to derive a role-based authorization policy from an existing policy. Given a set of users U , a set of permissions P , and a user–permission authorization relation \(\mathit {UPA} \subseteq U \times P\) , a role mining algorithm seeks to compute a set of roles R , a user–role authorization relation \(\mathit {UA} \subseteq U \times R\) , and a permission–role authorization relation \(\mathit {PA} \subseteq R \times P\) , such that the composition of UA and PA is close (in some appropriate sense) to UPA . Role mining is therefore a core problem in the specification of role-based authorization policies. Role mining is known to be hard in general and exact solutions are often impossible to obtain, so there exists an extensive literature on variants of the role mining problem that seek to find approximate solutions and algorithms that use heuristics to find reasonable solutions efficiently. In this article, we first introduce the Generalized Noise Role Mining problem (GNRM)—a generalization of the MinNoise Role Mining problem—which we believe has considerable practical relevance. In particular, GNRM can produce “security-aware” or “availability-aware” solutions. Extending the work of Fomin et al., we show that GNRM is fixed parameter tractable, with parameter \(r + k\) , where \(r\) is the number of roles in the solution and \(k\) is the number of discrepancies between \(\mathit {UPA}\) and the relation defined by the composition of \(\mathit {UA}\) and \(\mathit {PA}\) . We further introduce a bi-objective optimization variant of GNRM, where we wish to minimize both \(r\) and \(k\) subject to upper bounds \(r \le \bar{r}\) and \(k\le \bar{k}\) , where \(\bar{r}\) and \(\bar{k}\) are constants. We show that the Pareto front of this bi-objective optimization problem (BO-GNRM) can be computed in fixed-parameter tractable time with parameter \(\bar{r} +\bar{k}\) . From a practical perspective, a solution to BO-GNRM gives security managers the opportunity to identify a mined policy offering the best tradeoff between the number of policy discrepancies and the number of roles. We then report the results of our experimental work using the integer programming solver Gurobi to solve instances of BO-GNRM. Our key findings are that (a) we obtained strong support that Gurobi’s performance is fixed-parameter tractable, and (b) our results suggest that our techniques may be useful for role mining in practice, based on our experiments in the context of three well-known real-world authorization policies. We observed that, in many cases, our solver is capable of obtaining optimal solutions when the values of either k or r are small.

Best IDEAS: Special Issue of the International Database Engineered Applications Symposium

Database engineered applications cover a broad range of topics including various design and maintenance methods, as well as data analytics and data mining algorithms and learning strategies for enterprise, distributed, or federated data stores [...]

Strategic Transformation of E-Commerce Big Data Classification and Mining Algorithms Based on Artificial Intelligence Era

Strategic Transformation of E-Commerce Big Data Classification and Mining Algorithms Based on Artificial Intelligence Era

Optimization of frequent item set mining parallelization algorithm based on spark platform

In this paper, we propose a new method that combines the parallelism of the Spark-based platform with fast frequent mining, called STB_Apriori. Previous research has shown that traditional frequent itemset mining algorithms have high overhead when faced with large datasets and high-dimensional data computation, and generate a large number of candidate itemsets; at the same time, when faced with diverse user requirements, they often generate very sparse and diverse data. In order to solve the problem of fast mining of massive data, our idea originates from the capability of Spark distributed computing and the common optimisation ideas in Apriori mining, by using the efficient operator BitSet to achieve transaction compression, bit storage and data manipulation by Boolean matrices, and at the same time by parallelising the processing and optimising the algorithmic logic to achieve fast and frequent mining. In experiments on real-world datasets, our model consistently outperforms five widely used methods by a significant margin on very large data and maintains its excellence in the remaining cases, proving its effectiveness on real-world tasks, while further analysis shows that increasing the number of distributed nodes also incrementally and continuously improves performance.

An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior

In today's highly competitive market, organizations face significant challenges in accurately understanding and segmenting customer behavior due to the inherently dynamic and evolving nature of customer interactions over time. Traditional customer segmentation methods often neglect these temporal variations, leading to ineffective business strategies and missed opportunities. This research addresses this critical gap by introducing an innovative time series-based approach for customer behavior segmentation. By modeling each customer's behavior as a time series capturing key metrics such as purchase frequency, transaction amounts, and customer lifecycle costs the proposed method dynamically adapts to behavioral changes over time. To enhance segmentation precision, a genetic algorithm is employed to optimize feature weights, ensuring that the most relevant factors are emphasized. These optimized features are then clustered using spectral clustering to identify distinct and meaningful customer segments. The effectiveness of the proposed method is validated using 30 months of transactional data from a payment services company. The results demonstrate that the proposed approach, particularly when combined with spectral clustering and optimally weighted features, significantly surpassing the performance of traditional static segmentation techniques. This research not only provides a more accurate framework for uncovering hidden patterns in customer behavior but also delivers actionable insights for targeted marketing and personalized customer strategies.

An on-the-fly framework for usable access control policy mining

Access control policy mining, which extracts or infers access control policies from existing system logs or configurations within a given environment, is widely applied in policy migration. No policy mining algorithm can produce completely accurate policies, necessitating human intervention to correct errors before implementation. However, humans may fail to revise the produced policies without fully understanding all permission details. In this paper, we propose an on-the-fly framework to assist human revisions by mining usable policies. This framework is formulated as an approximate optimization problem, designed to avoid permission errors and redundancy by balancing submodularity and modular costs through an iterative search for access rules. The search space is guided by two pruning techniques: a tight optimistic estimate to only eliminate unpromising candidates and a queue cutter to sample promising candidates in advance. Experimental evaluations on two real-world and three publicly available synthetic datasets indicate that: 1) our method produces more concise results than existing methods, achieving a 93.2% reduction in redundancy; 2) our method is at least five times faster than state-of-the-art approaches. To further validate the usability of the policies obtained by our approach, we conducted a user study involving 30 participants and 7 large language models (LLMs). The results show that 90.9% of participants, including LLMs such as gpt-4o-mini, successfully modified our mined policies to meet given permission goals.