Data Mining Applications Research Articles

Locating Electrified Aircraft Service to Reduce Urban Congestion

The relentless expansion of urban populations and the surge in e-commerce have increased the demand for rapid delivery services, leading to an increase in truck traffic that contributes to urban congestion, environmental pollution, and economic inefficiencies. The critical challenge this poses is not only in managing urban spaces efficiently but also in aligning with global sustainability goals. This study addresses the pressing need for innovative solutions to reduce reliance on truck transportation in congested urban areas without compromising the efficiency of freight delivery systems. This study contributes a novel approach that leverages electrified and autonomous aircraft (EAA) cargo shuttles to shift the bulk of air transportable freight from road to air, specifically targeting underutilized airports and establishing vertiports in remote locations. By applying data mining techniques to analyze freight flow data, this research identifies key commodity categories and metropolitan statistical areas (MSAs) where the implementation of EAA services could significantly mitigate truck-induced congestion. The findings reveal that targeting a select few commodities and MSAs can potentially decrease truck traffic, with electronics emerging as the dominant commodity category, and cities like Los Angeles and Chicago as prime candidates for initial EAA service deployment. Stakeholders in urban planning, transportation logistics, and environmental policy will find this study’s insights beneficial. This work lays a foundation for future innovations in sustainable urban mobility and logistics.

The role of classifiers and data complexity in learned Bloom filters: insights and recommendations

Bloom filters, since their introduction over 50 years ago, have become a pillar to handle membership queries in small space, with relevant application in Big Data Mining and Stream Processing. Further improvements have been recently proposed with the use of Machine Learning techniques: learned Bloom filters. Those latter make considerably more complicated the proper parameter setting of this multi-criteria data structure, in particular in regard to the choice of one of its key components (the classifier) and accounting for the classification complexity of the input dataset. Given this State of the Art, our contributions are as follows. (1) A novel methodology, supported by software, for designing, analyzing and implementing learned Bloom filters that account for their own multi-criteria nature, in particular concerning classifier type choice and data classification complexity. Extensive experiments show the validity of the proposed methodology and, being our software public, we offer a valid tool to the practitioners interested in using learned Bloom filters. (2) Further contributions to the advancement of the State of the Art that are of great practical relevance are the following: (a) the classifier inference time should not be taken as a proxy for the filter reject time; (b) of the many classifiers we have considered, only two offer good performance; this result is in agreement with and further strengthens early findings in the literature; (c) Sandwiched Bloom filter, which is already known as being one of the references of this area, is further shown here to have the remarkable property of robustness to data complexity and classifier performance variability.

The Impact of Data Analytics and Mining in Transforming the Decision-Making System

In an increasingly data-driven world, effective decision-making hinges on the ability to use the power of information and technology supporting it. This research paper explores the transformative role of data analytics and mining in shaping the once upon a time decision support systems through a profound understanding and analysis of the existing research. This paper highlights the significance of leveraging data analytics and mining techniques to extract valuable insights from vast datasets. By uncovering hidden patterns and trends within the data, businesses can make informed and strategic choices that drive success and outperform their competitors. The practical applications of data analytics and mining are vast, including domains such as customer relationship management, fraud detection, and market analysis. The paper discusses the various ways in which these techniques enable organizations to obtain a competitive edge in the market by unlocking valuable data-driven insights. Real-world case studies are used to illustrate the tangible benefits of this approach. Overall, the research paper showcases the critical role of data analytics and mining in encouraging a data-driven decision-making culture across the business world. By equipping businesses with the analytical tools to unveil the true potential of their data, this approach paves the way for improved performance, sustained success and can guide organizations towards achieving their long-term objectives. Keywords: Data Analytics, Data Mining, Decision Making, Data, Technology, Information.

Application of Machine Learning and Data Mining in Manufacturing Industry

With the rise of machine learning in various industries, the traditional manufacturing industry is facing a new disruption, which requires the use of different technologies and tools to achieve its production targets; In this regard, machine learning (ML) and data mining (DM) play a key role. This paper provides a statistical understanding of the main methods and algorithms used to improve manufacturing processes over the past 20 years by dividing them into four main themes: Scheduling, Monitoring, Quality and Failure, presents previous ML research and the latest advances in manufacturing, followed by a comprehensive discussion of existing problem solutions in manufacturing from multiple aspects, It includes tasks (i.e., clustering, classification, regression), algorithms (i.e., support vector machines, neural networks), learning types (i.e., ensemble learning, deep learning), and performance indicators (i.e., accuracy, mean absolute error). In addition, the main steps of database knowledge discovery (KDD) process that should be followed in manufacturing applications are described in detail, and the methods to overcome some problems and the advantages of machine learning applied to manufacturing industry are briefly described. Finally, the paper summarizes and further looks forward to the future development direction.

An overview of the application of data mining in the campus

With the advent of campus informatization, it has been observed that the utilization of big data has the potential to facilitate the integration of education and enhance students' learning efficiency. The utilization of big data technology has been increasingly prevalent in contemporary society, leading to a growing reliance on its applications by the general population. Consequently, there is a rising need for educational platforms that can cater to this evolving demand. In the present scenario, China is persistently augmenting the development of campus infrastructure with the aim of attaining modern education objectives and ushering in a new era of educational facilities. The utilization of big data technology, its current status in contemporary usage, and the strategies for achieving widespread adoption have emerged as crucial concerns within the present wave of information technology throughout educational institutions. Simultaneously, the utilization of data mining technology enhances the learning environment for students, mitigates the constraints of campus intelligence, and offers substantial assistance for many educational endeavours inside the campus setting. The application of data mining technology for the advancement of campus management has emerged as a pivotal focal point and a significant breakthrough. This research examines the concept of the convenience zone in data mining and explores its application policy by conducting a comprehensive assessment of relevant literature. The study reveals that there is a lack of widespread awareness and adoption of big data in various contexts where it is necessary. Additionally, the potential utilization of big data at educational institutions, namely on campus, is explored.

Application of sensor-based speech data mining in E-commerce operations data analysis

With the rapid development of the e-commerce industry, enterprises need to effectively manage and analyze various operational data to support decision making and optimize business processes. Sensor technology is widely used in e-commerce environment, which can collect a lot of voice data and extract valuable information through data mining technology. The study presents the current background and trends of the e-commerce industry, with a focus on the importance and challenges of operational data analytics. A sensor-based voice data mining framework is proposed. Sensors are first used to collect voice data and turn it into a digital signal that can be used for analysis. Then the data mining algorithm is used to process and analyze the voice data, and a series of results about e-commerce operation are obtained. Analyze voice data to help companies develop effective marketing strategies, improve product quality, and optimize supply chain management. This study proves the role of sensor-based voice data mining in e-commerce operational data analysis. By harnessing the full potential of voice data, businesses can better understand and meet customer needs, improve competitiveness and performance. This research result has practical significance for the development of e-commerce industry, and provides guidance and reference for the research and practice in related fields.

Veri Madenciliğinde Kümeleme Teknikleri: Yöntemler, Zorluklar ve Uygulamalar Üzerine Bir Araştırma

Clustering is a crucial technique in both research and practical applications of data mining. It has traditionally functioned as a pivotal analytical technique, facilitating the organization of unlabeled data to extract meaningful insights. The inherent complexity of clustering challenges has led to the development of a variety of clustering algorithms. Each of these algorithms is tailored to address specific data clustering scenarios. In this context, this paper provides a thorough analysis of clustering techniques in data mining, including their challenges and applications in various domains. It also undertakes an extensive exploration of the strengths and limitations characterizing distinct clustering methodologies, encompassing distance-based, hierarchical, grid-based, and density-based algorithms. Additionally, it explains numerous examples of clustering algorithms and their empirical results in various domains, including but not limited to healthcare, image processing, text and document clustering, and the field of big data analytics.

Penerapan Metode Support Vector Machine Dalam Memprediksi Prediksi Cuaca

Weather is a natural that has a big impact on humans. Information about weather predictions is really needed by humans, especially in the city of Medan. This information is very useful for knowing weather events around us. Data mining is a process of collecting important information from large data, so that it can help make decisions and make accurate predictions. So researchers are interested in conducting research in predicting the weather in the city of Medan using the Support Vector Machine (SVM) method as a solution for predicting the weather in the city of Medan. The application of data mining using the SVM method helps produce precise accuracy for weather based on predetermined criteria. This method is suitable for weather predictions because it is able to provide clear and accurate assessments with weather predictions of 54.55%.

Educational Data Mining: Employing Machine Learning Techniques and Hyperparameter Optimization to Improve Students’ Academic Performance

Educational data mining (EDM) is a specialized field within data mining that focuses on extracting valuable insights from academic data across high school and university levels. A common practice in EDM involves predicting students’ grades to identify at-risk individuals and improve the efficiency of academic tasks. This knowledge benefits students, parents, and institutions equally. Early detection enables interventions that improve student performance. The literature presents various prediction strategies, each with its own unique advantages and disadvantages. This study aims to comprehensively evaluate the methods, tools, and applications of machine learning (ML) and data mining (DM) in education. The main goal is to improve the accuracy of predicting academic achievements by employing eight widely recognized ML algorithms: naïve bayes (NB), k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), logistic regression (LR), extreme gradient boost (XGBOOST), and ensemble voting classifier (EVC). The focus is on improving data quality by eliminating instances of noise. Performance evaluation involves assessing parameters such as accuracy, precision, F-measure, and recall. Incorporating cross-validation and hyperparameter tuning improves classification accuracy. The ML models outperform other ensemble approaches, providing a valuable tool for predicting student performance and assisting educators in making proactive decisions through timely alerts.

Evaluating Random Forest Algorithm in Educational Data Mining: Optimizing Graduation on-time prediction using Imbalance Methods

The study aims to evaluate the performance of Random Forest algorithms in data mining education by optimizing graduation on-time (GOT) predictions using imbalanced data methods. Methods used to handle imbalanced data include random under-sampling (RUS), random over-sampling (ROS), hybrids of RUS and ROS, synthetic minority over-sampling techniques for nominal classes (SMOTE-NC), and hybrids of SMOTE-NC and RUS. After applying these methods, studies analyze their performance on training and testing data. The research findings show that on training data, the RUS-ROS hybrid showed the best performance compared to other methods, while the SMOTENC and RUS hybrid techniques showed the best performance on testing data based on AUC values. The research showed that the use of an imbalanced data method significantly improved the ability of Random Forest algorithms to predict graduation on time (GOT) in the context of educational data. We discuss the implications for educational data mining applications and provide suggestions for future research.

Exploring school factors of academic achievement in Macau: The application of educational data mining in Progress in International Reading Literacy Study (PIRLS) 2016 assessment

International large-scale assessments, provide structured and static data, and due to their extensive databases, they can be considered as a valuable resource for Big Data in Education. In this paper, we propose an educational data mining approach to detect and analyze factors linked to academic performance in Macau schools using data from the Progress in International Reading Literacy Study (PIRLS) 2016. We conducted a secondary data analysis based on a set of socioeconomic, process, and outcome variables from PIRLS and other sources, and built decision trees to obtain a predictive model of school performance. By doing so, we were able to identify the school and student-level variables that are most significant in predicting student performance in Macau. These findings will be useful for informing educational policy decisions and shedding light on the causes of poor performance in Macau schools. Overall, our study highlights the potential of educational data mining approaches in analyzing large-scale assessment data and generating insights for educational research and practice.

Characterizing the water resource-environment-ecology system harmony in Chinese cities using integrated datasets: A Beautiful China perspective assessment

Integrated management and synergistic improvement of the water system is a topic of widespread concern. This study innovatively integrates three functions of quality assessment, synergy evaluation, and driving influence determination to establish a systematic framework assessing water system harmony. A case study of 336 Chinese cities is further performed by combining multi-scale and multi-source datasets. The results show China's water system quality has improved from 2015 to 2022. Development in the water resource, environment, and ecology subsystems have been differentiated, with 0.05 %, 4.33 %, and −1.64 % changes, respectively. The degradation of water ecology and the weak synergy with the other two subsystems have limited China's water system harmony. Water environment improvement played a contributive role in improving the water system quality. The contribution structure of water resources, environment, and ecology has shifted towards equilibrium in recent years. We found and highlighted the north-south differentiation of water system harmony in Chinese cities. The Beijing-Tianjin-Hebei and its surroundings, the Yangtze River Delta, and the middle reaches of the Yangtze River are identified as priority regions for water system harmony improvement. The primary contribution of this study is to propose an assessing concept of water resource-environment-ecology system harmony, establish well-structured assessment methods, and integrate the multiple data sources. The novel methods and findings, including the indicator system, application of data mining and decomposing methods, and the city-level water system harmony map, deconstruct and quantify the complex and diverse water system, supporting clearer and more efficient water management policymaking.

Network Representation of fMRI Data Using Visibility Graphs: The Impact of Motion and Test-Retest Reliability

Visibility graphs provide a novel approach for analysing time-series data. Graph theoretical analysis of visibility graphs can provide new features for data mining applications in fMRI. However, visibility graphs features have not been used widely in the field of neuroscience. This is likely due to a lack of understanding of their robustness in the presence of noise (e.g., motion) and their test-retest reliability. In this study, we investigated visibility graph properties of fMRI data in the human connectome project (N = 1010) and tested their sensitivity to motion and test-retest reliability. We also characterised the strength of connectivity obtained using degree synchrony of visibility graphs. We found that strong correlation (r > 0.5) between visibility graph properties, such as the number of communities and average degrees, and motion in the fMRI data. The test-retest reliability (Intraclass correlation coefficient (ICC)) of graph theoretical features was high for the average degrees (0.74, 95% CI = [0.73, 0.75]), and moderate for clustering coefficient (0.43, 95% CI = [0.41, 0.44]) and average path length (0.41, 95% CI = [0.38, 0.44]). Functional connectivity between brain regions was measured by correlating the visibility graph degrees. However, the strength of correlation was found to be moderate to low (r < 0.35). These findings suggest that even small movement in fMRI data can strongly influence robustness and reliability of visibility graph features, thus, requiring robust motion correction strategies prior to data analysis. Further studies are necessary for better understanding of the potential application of visibility graph features in fMRI.

Novel approach to energy consumption estimation in smart homes: application of data mining and optimization techniques

Buildings account for a significant portion of total energy consumption, and the introduction of intelligent buildings represents a significant step forward in efficiently managing energy utilization. The proposed solutions represent a significant step forward in the development of intelligent residential environments. Beginning the process of achieving improved building intelligence necessitates a thorough evaluation and prediction of the necessary heating and cooling energy requirements, taking into account all relevant influencing factors. This study describes methodologies for using data mining models to predict the heating and cooling energy requirements of intelligent buildings during the construction phase. Data mining techniques, specifically Support Vector Machines (SVM) and Random Forest, are used, demonstrating their superior efficiency over alternative methods. Metaheuristic algorithms, particularly the Owl Search Algorithm (OSA), are described as effective tools for optimizing results across a wide range of problem resolutions. OSA is described and proposed alongside novel data mining methods, demonstrating that this combination of algorithms improves the performance of Random Forest and SVM-based models by 11% and 24%, respectively. The proposed models can generate predictions with a small number of parameters, eliminating the need for complex software and tools. This user-friendly approach makes the prediction process more accessible to a wider audience. While specialized equipment and professional-grade tools will be used, the proposed models are accessible to a wide range of individuals interested in participating in the prediction process.

Boosting HPC data analysis performance with the ParSoDA-Py library

Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications.

Hadoop-Based Political and Ideological Big Data Platform Architecture and Pattern Mining in Higher Education

There is a growing opportunity for self-improvement within the realm of higher education, represented by the combination of big data technologies and higher education. The Education Big Data Processing Framework (EBDPF) is the name of the platform that is the subject of this article, which focuses on the development of an advanced big data processing platform. In order to process huge datasets in an effective manner, the platform makes use of Hadoop's big data storage architecture, in addition to Hive, Flume, and Sqoop for data collection and synchronization, respectively. In this study, the implementation of standard data mining techniques is investigated through the use of MapReduce programming. More specifically, the study investigates the execution efficiency and scalability of these algorithms within the Hadoop platform. The data clustering task in data mining is chosen as a representative problem in this study so that the effectiveness of the EBDPF may be evaluated accordingly. An implementation of the clustering job that is based on MapReduce is created and tested on the Hadoop platform. Extensive experiments are conducted with a range of cluster sizes and data sizes. The findings provide more evidence that Hadoop distributed systems are more efficient and successful than traditional methods when it comes to managing data mining applications. The extended performance analysis of computing power not only provides evidence that the platform is currently effective, but it also provides a signal that it has a significant amount of potential that has not yet been utilized. At the convergence of big data technology and higher education, the EBDPF emerges as a strong and promising framework, demonstrating considerable steps in accelerating data processing within educational contexts. In conclusion, the EBDPF is a framework that demonstrates significant progress.

Measuring the similarity between shapes of buildings using graph edit distance

ABSTRACT Building data play a crucial role in geographic information science, and building shape similarity can be used for spatial inquiry, cartographic generalization, map updating, data quality assessment, and other spatial data mining applications. However, conventional shape representation and measurement methods use geometric and statistical measures and may not effectively capture the subtle differences among building shapes. Geospatial artificial intelligence methods often overlook nuanced differences in building contours, posing a challenge when precise fine-grained measurements are required. Furthermore, they have low interpretability. This study proposes a graph edit distance-based method for measuring the similarity between building shapes. It uses geometrics characteristics to construct a graph, matches and associates similar regional features between two building contours, and defines edit costs based on geometric and positional differences that align with the Gestalt principle of visual similarity. It converts the optimal edit path problem to the maximum weighted cluster solution problem and utilizes a heuristic approach to achieve the exact optimal solution within a moderately suitable timeframe. The experimental results demonstrated that the proposed method identifies detailed differences between building contours, providing accurate similarity measurement with high interpretability.

Construction and Innovative Application of Fundamental Training Educational Resource Repository Using Data Mining Techniques

With the rapid advancement of information technology and data mining methodologies, the educational sector is undergoing an unprecedented transformation. The increasing proliferation of online educational resources offers significant advantages for educators and learners, yet presents challenges in resource retrieval and recommendation. Existing query and recommendation systems frequently rely on simplistic keyword matches and browsing history, struggling to cater to the escalating demand for personalized learning experiences. This study introduces an innovative method for constructing a Fundamental Training Educational Resource Repository (FTERR) leveraging data mining techniques. Firstly, students’ learning needs are intricately captured using knowledge graph and semantic analysis methods. Secondly, addressing the challenges of resource storage and retrieval, a novel data storage model is proposed, which, when paired with cutting-edge clustering algorithms, facilitates rapid and visualized retrieval of educational materials. This research not only furnishes novel methodologies for data mining applications in the educational domain but also fosters the advancement of educational informatization.

Application and Feasibility Analysis of Network Models and Data Analysis in Combating Organized Crime

This article discusses and analyzes in detail the development potential and application of big data mining and network visualization analysis in combating organized crime. Firstly, the article emphasizes the importance of combating criminal gangs and organized crime in modern society, comparing them to the foundation of stable national development and the cornerstone of national progress. Then it explores the security challenges faced by modern society and more issues related to organized crime, including the problem of crime caused by the wealth gap caused by urbanization and economic development, as well as the outdated technology for cracking down on existing security crimes. This article provides a detailed introduction to the potential of big data mining and network visualization, as well as the advantages of these technologies in practical applications. It covers the collection, processing, sharing, and application of data. At the same time, network visualization is used to visualize crime data, helping decision-makers better understand the current situation and problems of public security and organized crime. The article also discusses the application of artificial intelligence and mobile internet technology in combating organized crime. And the potential of big data in improving urban governance. Finally, this article looks ahead and emphasizes that big data mining and network visualization analysis will continue to play an important role in optimizing social security, indicating the broad prospects of these technologies in the future transportation field.

Penerapan Data Mining Algoritma Apriori untuk Menemukan Pola Hubungan Status Gizi Balita

Malnutrition of toddlers can cause severely health problems, growth retardation, and decreased intelligence. Therefore, it is important to know the pattern of the relationship between the nutritional status of toddlers, especially against the condition of malnutrition to get information about the possible risk of nutritional coexistence problems. The purpose of this research is to apply data mining using the apriori algorithm in finding patterns of relationships betweennutritional status and to produce an accuracy level of association rules based on the value of support, confidence, and validation with the lift ratio. The results of this study are expected to generate information that can help prevent and treat malnutrition, as well as appropriate nutrition interventions. Analysis and implementation of data using the a priori algorithm method. Manual calculations are carried out using Microsoft Excell and accurate calculations with python programming. Data on the nutritional status of children under five, especially undernourished conditions will be processed to find patterns of association relationships. Determined the minimum support value of 0.1 or 10% and the minimum confidence of 0.5 or 50%. The research produces association rules in the form of association relationship patterns of nutritional status specifically malnutrition conditions (Underweight?Stunting ) with a support of 0.25 or 25% and confidence 0.57 or 57%. The rules have met the minimum support and minimum confidence values and have been validated by the lift ratio with a value &amp;gt; 1.