Data Mining Process Research Articles

Employing educational data mining techniques to predict programming students at-risk of dropping out

This research primary aimed at evaluating various predictive models in predicting programming students at risk of dropping out. It also aimed at identifying attributes that are significant in predicting students at risk of dropping. The educational data mining process (EDM) was utilized as the research framework. The study conducted a ten-fold cross-validation, revealing that the k-nearest neighbors (kNN) algorithm achieved the highest classification accuracy at 95.5%. The decision tree model followed closely with a 94.9% accuracy, logistic regression exhibited 94.4%, and the neural network model yielded a classification accuracy of 93.2%. Further analysis, including confusion matrices and receiver operating characteristic (ROC) curves, provided detailed insights into the models' performance. Notably, the decision tree algorithm excelled in identifying students who did not drop out, with a misclassification rate of 9 out of 30 for dropped students. Analysis also showed that students’ assignments completed (AC), laboratory work (LW), and attendance (ATT) were the strongest predictors in identifying students at risk of dropping. Results of the study can be used by instructors to identify in advance student at risk of dropping and provide them with the necessary intervention to improve performance in programming.

A Comparative Analysis of Machine Learning Algorithms for Predicting the Telemarketing Campaigns of Portuguese Banking Institutions

A financial institution is an entity of business that handles deposits, money circulation, saving, and other corresponding operations. Banks supply a broad spectrum of services. This differs depending on the capacity of each bank. The bank is going to provide further amenities, which is one of the many industries that frequently engage in direct product launches. Thus, banks might employ technological innovations to do market investigation before directly releasing products, which may assist with decision-making. Marketing initiatives that offer prospective consumers the option of embracing or rejecting an offered product can be carried out using direct email, phone calls, and email campaigns. The volume of incoming data continues to expand as time goes by. One of the bank institutions in Portugal faced difficulties judging whether or not its customer base would choose to sign up for a term deposit because of the growing amount of data. Consequently, to ascertain if the consumer will subscribe to a term deposit, the data mining processes utilized here will incorporate Ensemble Learning (Gradient Boosting (GB), Xg Boost (XGB), Random Forest (RF), Linear Models (Logistic Forest (RF), Linear Models (Logistic Regression (LR), Non-Linear Models (K-Near Neighbors (KNN), Support Vector Machine (SVM), Decision Tree Classifier (DT)), and Probabilistic Models (Naive Bayes (NB))included in classification techniques. Among these, Xg Boost yields the most impressive results, with the best performance in terms of AUC, model accuracy, and cross-validation accuracy in a minimum amount of computation time.

Supporting SME companies in mapping out AI potential: a Finnish AI development case

AbstractProducts and services relying upon Artificial Intelligence (AI) have moved from mere concepts to reality. However, challenges still exist in applying AI technologies to traditional industrial and service enterprises. Two central problems are a proper understanding of the opportunities AI could bring to the business processes and making the business logic and data sources transparent to AI experts. As small and medium-sized enterprises (SMEs) are considered the economic backbone of many countries, this paper studies how to support SMEs in understanding the potential of AI in their business and how to prepare their data and requirements for a possible AI project. For this purpose, we first proposed the Cross-Industry Standard Process for Data Mining (CRISP-DM) an industry-proven way to apply AI solutions. The weight was in early business and data understanding. Then, we performed data visualization and developed some machine learning methods for 11 SMEs in South-western Finland as case studies to get more ideas for improving their business using AI. Two surveys probed the possible changes in AI practises of companies.

Developing an intelligent and sustainable model to improve E-learning satisfaction based on the learner’s personality type: data mining approach in high education systems

PurposeAlthough E-learning has been in use for over two decades, running parallel to traditional learning systems, it has gained increased attention due to its vital role in universities in the wake of the COVID-19 pandemic. The primary challenge within E-learning pertains to the maintenance of sustainable effectiveness and the assurance of stakeholders' satisfaction. This research focuses on an intelligence-driven solution to recommend the most effective approach to education policymakers by considering the unique characteristics of all components within the educational system (course type, student and teacher characteristics, and technological features) to achieve a sustainable E-learning system.Design/methodology/approachThrough a systematic literature review and qualitative content analysis, a conceptual model of the critical components influencing E-learning quality and satisfaction has been developed. The proposed model comprises six main dimensions: usage, service quality, learning system quality, content quality, perceived usefulness, and individual characteristics. These dimensions are further divided into 15 components and 114 sub-components. A data mining process encompassing two scenarios has been designed to prioritize the components impacting E-learning success.FindingsIn the first scenario, data mining techniques identified the most influential components based on the features outlined in the conceptual model. According to the results, the components affecting E-learning quality enhancement in the studied case are “usage purpose, system loyalty, technical and supportive system quality, and student characteristics.” The second scenario examines the impact of individuals' personality types and learning styles on E-learning satisfaction across various aspects (Average System Satisfaction, Overall System Satisfaction, Efficiency and Effectiveness, Skill Enhancement, and Personal Enhancement). The findings reveal that, with an accuracy of over 70%, E-learning satisfaction for diplomat and guard learners is influenced by the alignment between “course learning style” and “student-suggested course learning style.” Conversely, for analyzer and researcher types, satisfaction levels are impacted by the “learning style compatible with their personality type.”Originality/valueImplementing a dynamic model founded on data mining enables educational institutions to personalize the E-learning experience for each individual as much as possible. The study’s findings indicate that “achieving higher satisfaction levels in the E-learning process is not necessarily contingent upon providing a learning style congruent with learners' personality types.” Rather, perceived and suggested learning styles exert a more profound influence. Consequently, providing prescriptive principles for higher education institutions seeking to enhance E-learning quality is inadvisable. Instead, adopting a dynamic, knowledge-based process that furnishes recommendations to policymakers for each course—tailored to the specific course type, teaching records, current processes and technology, and student type—is highly recommended.

Analisis Pola Pembelian Pelanggan Menggunakan Algoritma Squeezer, Apriori dan FP-Growth Pada Toko Bangunan

To compete with other stores, store owners need to design various strategies, one of which is understanding customer purchase patterns. This article examines the Squeezer algorithm and compares the performance of the Apriori and FP-Growth algorithms in forming customer purchase association patterns that can be used as a reference for store owners in planning sales strategies. The data mining process was carried out using Association Rules and Clustering methods. A total of 1256 sales transaction data samples were analyzed to understand the association patterns produced by each method. Based on the test results with a minimum support of 0.2 and a confidence of 0.6, the Apriori algorithm produced 194 association rules with a total rule strength of 1.16. Meanwhile, the FP-Growth algorithm produced 52 association rules with the same total rule strength of 1.16. The Clustering Method resulted in 7 clusters with a similarity value of 0.06322. After comparison, the FP-Growth algorithm proved to have better performance in generating association rules compared to the Apriori algorithm.

Enhancing SMEs digital transformation through machine learning: A framework for adaptive quality prediction

As smart manufacturing expands, businesses see the importance of digital transformation, especially for small and medium-sized enterprises (SMEs). Unlike larger companies, SMEs face greater challenges when undergoing digital transformation due to technological questions. However, recent advancements in high-performance computing and reduced hardware costs have made deep learning-based digital transformation more financially feasible for SMEs. While previous research utilized machine learning for product quality prediction, there remains a lack of comprehensive in adaptive quality prediction specifically designed for SMEs. This study presents a systematic framework utilizing various machine learning methods and validates research cases using CRISP-DM (Cross-Industry Standard Process for Data Mining). The first step involves applying XGBoost(eXtreme Gradient Boosting)for feature selection, the second step utilizes GRU for parameter prediction. Finally, in the third step, SVM (Support Vector Machine) is employed for quality classification. The integrated framework achieves high accuracy, with R2reaching 90 % for predicted parameters and nearly 95 % for classification indicators. Moreover, this research addresses the research gap in quality prediction and adaptability and provide an effective digital transformation solution for SMEs without substantial investment. The proposed research framework can be applied SMEs of other domains, such as the machining and traditional manufacturing industry.

Data mining process to detect suicidal behaviour in out-of-hospital emergency departments

Out-of-hospital emergency departments receive multiple types of requests daily. Their management requires a balance to be found between available resources and the actual needs of the requesting party. Those regarding suicidal behaviour, which are resource heavy, are few in number in terms of the bulk of requests, and detecting them correctly is therefore important. Previous research, using machine learning algorithms to analyse suicide, has typically focused on discovering insights to be used by medical personnel. This proposal extends its use in two directions: knowledge that can be used by non-exclusively medical staff, such as telephone operators, and the models that have been incorporated into a software prototype to help in the decision-making of an emergency department. In addition, previous research has often included a range of information from different sources that are not available when processing an emergency call request, for example, data that is only obtained at the end of the intervention. A full-scale data mining process has been performed using data from the out-of-hospital emergency service in Malaga (Spain). Sensitivity has been the primary goal to avoid missing cases requiring special attention, but this objective has been pursued without overlooking a good trade-off with specificity. The best models can offer such a compromise between sensitivity and specificity, and show more than 80% in both metrics simultaneously. The experts validate that the modelling phase showed that the algorithms have automatically identified already known situations. This lays the groundwork for further iterations with a promising outlook.

A cost modelling methodology based on machine learning for engineered-to-order products

Recent scientific studies are targeted at applying and assessing the effectiveness of Machine Learning (ML) approaches for cost estimation during the preliminary design phases. To train ML prediction models, comprehensive and structured datasets of historical data are required. This solution is inapplicable when such information is unavailable or sparse due to the lack of structured datasets. For engineered-to-order products, the number of historical records is often limited and strongly influenced by different purchasing or manufacturing strategies, thus requiring complex normalisation of such data.This method overcomes the above limitations by presenting an ML-based cost modelling methodology for the conceptual design that is applicable even when historical data are insufficient to train the prediction algorithms. The training dataset is generated through an analytical and automatic software tool for manufacturing cost estimation. Such a tool, starting from a 3D model of a product, can quickly and autonomously assess the related cost in different scenarios. An extensive and structured training dataset can be easily generated. The proposed methodology was based on CRISP-DM (Cross Industry Standard Process for Data Mining).Cost engineers of an Oil & Gas company used the method to develop parametric cost models for discs and spacers of an axial compressor. The solution guarantees lower error (7% vs 9%) and significant time-saving (minutes instead of hours) than estimations based on other approaches. Cost models are more comprehensive (capable of analysing different scenarios), explainable (not conceived as a black box), and self-learning (can be updated by extending the training dataset).

An Ensemble Machine Learning and Data Mining Approach to Enhance Stroke Prediction.

Stroke poses a significant health threat, affecting millions annually. Early and precise prediction is crucial to providing effective preventive healthcare interventions. This study applied an ensemble machine learning and data mining approach to enhance the effectiveness of stroke prediction. By employing the cross-industry standard process for data mining (CRISP-DM) methodology, various techniques, including random forest, ExtraTrees, XGBoost, artificial neural network (ANN), and genetic algorithm with ANN (GANN) were applied on two benchmark datasets to predict stroke based on several parameters, such as gender, age, various diseases, smoking status, BMI, HighCol, physical activity, hypertension, heart disease, lifestyle, and others. Due to dataset imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied to the datasets. Hyperparameter tuning optimized the models via grid search and randomized search cross-validation. The evaluation metrics included accuracy, precision, recall, F1-score, and area under the curve (AUC). The experimental results show that the ensemble ExtraTrees classifier achieved the highest accuracy (98.24%) and AUC (98.24%). Random forest also performed well, achieving 98.03% in both accuracy and AUC. Comparisons with state-of-the-art stroke prediction methods revealed that the proposed approach demonstrates superior performance, indicating its potential as a promising method for stroke prediction and offering substantial benefits to healthcare.

A Multi-dimensional Data Mining-based Study on the Prescriptions Developed by Professor Xu Zhiyin in Treating Thyroid Nodules.

In this study, we employed a multi-dimensional data mining approach to examine the clinical instances where Professor Xu Zhiyin treated thyroid nodules. Our aim is to understand the patterns of symptoms, underlying causes, and treatment approaches used for thyroid nodules. By doing so, the intention is to distill the essential aspects, compile Professor Xu Zhiyin's clinical insights, and investigate his scholarly perspectives. Professor Xu Zhiyin's clinical diagnoses and treatments spanning from 2009 to 2019 were entered into Microsoft Excel. Subsequently, the collected data was imported into the Medcase V5.2 system to facilitate data mining. Various techniques, such as frequency-based method, association rule analysis, and clustering, including a decentralized system clustering approach, were employed on a set of 346 cases involving patients with thyroid nodules that conformed to the specified criteria. The primary focus was on extracting insights regarding symptoms and the underlying causes from the medical records. By integrating these findings with Professor Xu Zhiyin's clinical expertise, we examined and summarized the outcomes of the data mining process. The fundamental prescriptions were successfully extracted using the techniques for mining across multiple dimensions. Utilizing the scattered grouping of these prescriptions and with reference to the cluster analysis of the frequency-linked system, the fundamental prescriptions proposed by Professor Xu Zhiyin for addressing thyroid nodules encompass the following ingredients: Glycyrrhiza uralensis Fisch, Cortex Moutan, Paeoniae radix rubra, Curcuma longa L., Radix Curcumae, persica seed, Citri Reticulatae Viride Pericarpium, Pinellia ternata, Spica Prunellae, Ostreae concha, Gleditsia sinensis spine, Tuckahoe and Radix Codonopsis. The fundamental prescriptions were acquired using the frequency approach, association rule technique, k-means clustering approach, and systematic clustering approach. The research findings corroborate one another, demonstrating that Professor Xu Zhiyin's approach to distinguishing and treating thyroid nodules is embodied in distinct prescriptions tailored to specific diseases.

Research Trends In Multimodal Critical Discourse Analysis: A Bibliometric Analysis

This research aims to understand the evolution and development of Critical Discourse Analysis (CDA) research with a focus on multimodality. CDA is an interdisciplinary approach that explores the use of language and discourse in reproducing, reinforcing, and being closely linked to social relations, power, and ideology. From the initial understanding introduced by Norman Fairclough in the 1980s, CDA has become an important tool for analyzing power structures and social constructions in various contexts, including media, politics, education, and popular culture. CDA has undergone significant transformations along with complex changes in the social and political world. This research aims to map the latest trends and developments in CDA studies, with a special focus on aspects of multimodality. The data collection process is carried out by data mining process in Scopus using keywords and search limitations per category Subject area; Doc.type; Pub.stage; Source type; Language. The document data collected were 303 Scopus indexed documents. By involving bibliometric methods through the Vosviewer tool, the data was visualized and analyzed. The results of the study found that the development of CDA research in the aspect of multimodality has increased in number every year. The study of CDA in terms of multimodality began in 1993 and began to experience an increase in the number of studies from 2013 to 2023. The scope of keywords that emerged was six clusters. Each cluster of keywords reflects special attention to the scope in the study of critical discourse analysis and multimodality, including the diversity of research themes, the intersection of media and social aspects, the importance of visual and multimodal analysis, the connection with the local context, and the application of critical theories and concepts.

Patterns and factors associated with dental service utilization among insured people: a data mining approach

BackgroundInsurance databases contain valuable information related to the use of dental services. This data is instrumental in decision-making processes, enhancing risk assessment, and predicting outcomes. The objective of this study was to identify patterns and factors influencing the utilization of dental services among complementary insured individuals, employing a data mining methodology.MethodsA secondary data analysis was conducted using a dental insurance dataset from Iran in 2022. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was employed as a data mining approach for knowledge extraction from the database. The utilization of dental services was the outcome of interest, and independent variables were chosen based on the available information in the insurance dataset. Dental services were categorized into nine groups: diagnostic, preventive, periodontal, restorative, endodontic, prosthetic, implant, extraction/surgical, and orthodontic procedures. The independent variables included age, gender, family size, insurance history, franchise, insurance limit, and policyholder. A multinomial logistic regression model was utilized to investigate the factors associated with dental care utilization. All analyses were conducted using RapidMiner Version 2020.ResultsThe analysis encompassed a total of 654,418 records, corresponding to 118,268 insured individuals. Predominantly, restorative treatments were the most utilized services, accounting for approximately 38% of all services, followed by diagnostic (18.35%) and endodontic (13.3%) care. Individuals aged between 36 and 60 years had the highest rate of utilization for any dental services. Additionally, families comprising three to four members, individuals with a one-year insurance history, people contracted with a 20% franchise, individuals with a high insurance limit, and insured individuals with a small policyholder, exhibited the highest rate of service usage compared to their counterparts. The regression model revealed that all independent variables were significantly associated with the use of dental services. However, the patterns of association varied among different service categories.ConclusionsRestorative treatments emerged as the most frequently used dental services among insured individuals, followed by diagnostic and endodontic procedures. The pattern of service utilization was influenced by the characteristics of the insured individuals and attributes related to their insurance.

Enhancing quality 4.0 and reducing costs in lot-release process with machine learning-based complaint prediction

PurposeThis study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.Design/methodology/approachThis study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.FindingsThe framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.Practical implicationsThe framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.Originality/valueTo the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

STRATEGIC DECISION SUPPORT: DEVELOPING A DASHBOARD FOR MANAGERIAL INSIGHTS IN HIGH-LEVEL TVET STUDENTS' ENROLMENT AT UNIVERSITI MALAYSIA PAHANG AL-SULTAN ABDULLAH

The purpose of this study was to create a dashboard with information visualisation to assist Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) in managerial decision-making and to critically and hierarchically present data regarding the enrolment of Malaysian Diploma Vocational (MDV) students into high-level Technical Vocational Education and Training (TVET) programs at UMPSA. The Cross Industry Standard Process for Data Mining (CRISP-DM) model was applied in this research followed by dashboard development using Microsoft Power BI. The CRISP-DM model consists of six continuous and connected phases, namely business understanding, data understanding, data preparation, data modelling, data visualisation, and data synchronise and update. The data was obtained from the Undergraduate Records Management Unit, Centre for Academic Management, UMPSA. The database was used in the development of the dashboard to store information in the form of reports and for data visualisation purposes. The findings hope to assist the management of UMPSA in making better decisions and planning the enrolment process of prospective students. The dashboard outlines several initiatives and best practices that should be implemented by the Malaysian Government, the Ministry of Higher Education, and UMPSA through the UMPSA 2021–2025 Strategic Plan to empower the high-level TVET agenda.

ENHANCING LIFELONG LEARNING: A DATA-DRIVEN DASHBOARD APPROACH FOR STRATEGIC DECISION-MAKING AT UNIVERSITI MALAYSIA PAHANG AL-SULTAN ABDULLAH

The purpose of this study was to develop a dashboard with information visualisation for the lifelong learning (LLL) programs at Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) and provide recommendations for UMPSA to improve the strategies and decision-making for future LLL program offerings. This study used the Cross Industry Standard Process for Data Mining (CRISP-DM) model created by the International Business Machines Corporation (IBM) and dashboard development using Microsoft Power BI. The updated database was created by obtaining data from the Institute of Graduate Studies (IPS), UMPSA. Such data was then prepared through the process of selecting, cleaning, constructing, integrating, and formatting data. The dashboard was developed using a database for data visualisation and information in the form of reports. Such information can ensure a better decision-making process and the UMPSA management’s readiness in planning the admission process for future students. However, the data management and data quality at UMPSA must be improved. Future research can expand the present investigation by including data from all categories of LLL students at UMPSA to facilitate a more comprehensive analysis. It will also enable the university management to make accurate decisions about program offerings in the future.

An Exploration of the Development of Computerized Data Mining Techniques and Their Application

The process of data mining involves extracting valuable information and knowledge from vast amounts of data, encompassing various fields such as statistics, machine learning, and database theory. It transcends mere data processing tools, employing intelligent methods for data analysis and stands as a pivotal technology in the era of big data. Its applications span across numerous domains including consumer behavior analysis, market marketing strategies, risk management, medical diagnosis, and fraud detection, with notable prominence in the realm of commerce. In e-commerce platforms, data mining techniques adeptly analyze user purchase histories and browsing records to precisely identify user preferences, recommend personalized products, thus enhancing user satisfaction and sales revenue. In devising marketing strategies, analyzing market data and consumer feedback optimizes product promotion strategies, bolstering market competitiveness. In the medical domain, data mining exhibits robust vitality. By analyzing patient medical records, genetic data, drug response data, predictions of disease trends, optimization of treatment plans, and even early disease warnings are made possible, thereby improving disease cure rates. In the realm of financial security, data mining plays a pivotal role by analyzing transaction data and user behavior to timely detect abnormal transactions and identify fraudulent activities, thereby safeguarding the security and stability of the financial system. In the Internet of Things (IoT) domain, data mining techniques analyze massive sensor data, enabling real-time monitoring and predictive maintenance of devices, thus enhancing operational efficiency and longevity. Data mining extends beyond the aforementioned domains, finding significant applications in social governance, scientific research, intelligent manufacturing, and more. Not only does data mining enhance the scientific rigor and precision of decision-making, but it also empowers various industries to improve efficiency, realize intelligent transformations, thereby further advancing societal progress.

FPAS Model: A Case Study of Flood Prediction and Advisory System

The increasing frequency of flood events worldwide is a significant concern, particularly for the United Nations (UN), as it impacts both economic stability and public safety. In recent decades, floods have caused extensive damage to lives and properties, and projections indicate this trend will continue. Rapid evacuation in the event of a flood relies on effective early warning systems that can predict floods and encourage residents to evacuate high-risk areas. Africa is the third most affected continent by floods, following Asia and Europe. However, the development and implementation of flood forecasting models in Africa are still in the early stages. The continent has only a few hydrological models for flood forecasting, and most of these need improvements to keep pace with changing conditions. The study aimed to enhance flood prediction and advisory systems by leveraging machine learning (ML). The objectives are to develop a predictive model, create dual subsystems for web and Android applications, disseminate predictions and advisories through multiple communication channels, and incorporate persuasive messages to prompt at-risk populations to evacuate. A survey was conducted in Cross River State to evaluate the impact of these persuasive messages and the overall support for the Flood Predictive and Advisory System (FPAS). The study utilized the NiMet dataset, preprocessed the data, and trained the model using SVM, Random Forest, and XGBoost algorithms. Subsequently, both the FPAS web and Android applications were developed and rigorously tested. The research methodology applied in this work was a hybrid approach, combining Object Oriented Analysis and Design Methodology (OOADM) and the Cross Industry Standard Process for Data Mining (CRISP-DM). OOADM was used to develop the mobile app for citizens’ registration and a web app for administrative activities, while CRISP-DM was used to create a data-drive predictive model for the app. These applications facilitated citizen registration and administrative activities, providing feedback via websites, emails, and automated alerts. The survey results revealed that 76.56% of respondents had not experienced persuasive messages before, while 91.15% expressed strong support for the FPAS system incorporating these techniques. Finally, the study successfully developed an advanced flood prediction model and an integrated advisory system, demonstrating significant potential in enhancing disaster preparedness and response through innovative technology and strategic communication.

Comparison Of K-Means and K-Medoids Algorithm for Clustering Data UMKM in Pagar Alam City

The aim of this research is clustering MSME data in Pagar Alam City using the K-Means and K-Medoids algorithms. This research is motivated by the lack of further management of MSME data collection, which can hinder the development and improvement of Pagar Alam City MSMEs. Meanwhile, this data is considered necessary for agencies to develop and improve Pagar Alam City MSMEs. Apart from agencies, this data is also useful for sub-districts, sub-districts and RT/RW to find out what interests, talents and potential the community has in what business fields. MSME data is processed using Rapid Miner and Python, the system development method in this research uses the Cross Industry Standard Process for Data Mining (CRISP-DM) method, where the stages include Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The test method uses the Davies-bouldin index, a DBI value that is close to 0 results in good clustering. The results of this research obtained 3 clusters. In 2020 K-Means C0= 1, C1= 3 and C2= 1 sub-district, K-Medoids C0= 1, C1= 1 and C2= 3 sub-district. In 2022 K-Means C0= 1, C1= 3 and C2= 1 sub-district, K-Medoids C0= 1, C1= 3 and C2= 1 sub-districts. The results of the 2020 sub-district DBI clustering calculation are DBI k-means = 0.134 and k-medoids = 0.523. In 2022 DBI k-means = 0.277 and k-medoids = 0.496. So it can be concluded that the K-Means algorithm in the case of grouping MSMEs in Pagar Alam City has better performance, because the DBI value is close to 0. From the results of the grouping it can help provide an overview for related parties in encouraging or providing assistance to sub-districts that are included in the low cluster.

Research on Evaluating Information Security Level Protection by Data Mining Method

Information technology’s continuous development and progress can bring great changes to society, which is also born in the hierarchical protection of information systems. Among them, grade protection evaluation is the key part, and methods to find useful information from its complicated evaluation data to assist decision-making have become an important content of research. Aiming at the evaluation process of data mining, this article intends to analyze the classical algorithms of association rules. The Apriori algorithm and FP-growth, two common and representative algorithms, are chosen as a comparison. Finally, it is found that the performance of the latter is better than that of the former. The rationality of association rules is proved by analysis to better assist the evaluators in the evaluation work and reduce the misjudgment of the evaluation results.

Analysis of the Spread and Evolution of COVID-19 Mutations in Ecuador Using Open Data.

Currently, the analyses of and prediction using COVID-19-related data extracted from patient information repositories compiled by hospitals and health organizations are of paramount importance. These efforts significantly contribute to vaccine development and the formulation of contingency techniques, providing essential tools to prevent resurgence and to effectively manage the spread of the disease. In this context, the present research focuses on analyzing the biological information of the SARS-CoV-2 viral gene sequences and the clinical data of COVID-19-affected patients using publicly accessible data from Ecuador. This involves considering variables such as age, gender, and geographical location to understand the evolution of mutations and their distributions across Ecuadorian provinces. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology is applied for data analysis. Various data preprocessing and statistical analysis techniques are employed, including Pearson correlation, the chi-square test, and analysis of variance (ANOVA). Statistical diagrams and charts are used to facilitate a better visualization of the results. The results illuminate the genetic diversity of the virus and its correlation with clinical variables, offering a comprehensive understanding of the dynamics of COVID-19 spread in Ecuador. Critical variables influencing population vulnerability are highlighted, and the findings underscore the significance of mutation monitoring and indicate a need for global expansion of the research area.