Application Of Data Mining Methods Research Articles

Machine Learning based on Probabilistic Models Applied to Medical Data: The Case of Prostate Cancer

The growth in the amount of data in companies puts analysts in difficulties when extracting hidden knowledge from data. Several models have emerged that focus on the notion of distances while ignoring the notion of conditional probability density. This research study focuses on segmentation using mixture models and Bayesian networks for medical data mining. As enterprise data becomes large, there is a way to apply data mining methods to make sense of it using classification methods. We designed different models with different architectures and then applied these models to the medical database. The algorithms were implemented for the real data. The objective is to classify individuals according to the conditional probability density of random variables, in addition to identifying causalities between traits from tests of conditional independence and a correlation measure, both based on χ2. After a quick illustration of several models (decision tree, SVM, K-means, Bayes), we applied our method to data from an epidemiological study (done at the University of Kinshasa University clinics) of case-control of prostate cancer. Thus, we found after interpretation of the results followed by discussion that our model allows us to classify a new individual with an accuracy of 96%.

Features of Recommendation Algorithm on Base of Analysis of Social Network Data Mining Methods

In recent years, social media platforms have become powerful data collection tools to improve user experience. The vast amount of data generated through social media provides a unique opportunity to develop innovative recommendation systems. This article analyzes the application of data mining methods for social networks in the context of effective recommendation systems, focusing on three key methodologies: sentiment analysis (SA), topic modeling (TM) and social network analysis (SNA), highlighting their positive features. SA allows the system to tailor recommendations based on sentiment analysis, offering users items that match their expressed emotions. Experiments show significant improvements in recommendation accuracy when sentiment data is integrated. TM allows the system to understand the main concerns of users by identifying dominant themes, thereby providing personalized recommendations and staying abreast of evolving trends. At the same time, AFM identifies influential users and communities, increasing relevance and awareness of system elements. The article highlights the enormous potential of social networks for the development of effective, personalized recommendation systems. Using sentiment analysis and topic modeling, these systems can provide personalized and relevant recommendations based on public sentiment, trending topics, and social network dynamics.

A hybrid data-driven approach for the analysis of hydrodynamic lubrication

The application of data mining technology has intensively advanced tribology research. While recent lubrication studies have highlighted the importance of data mining, researchers have not fully bridged the gap between massive lubrication data and intrinsic lubrication mechanisms. Thus, by revisiting lubrication modelling from the data-driven and physics-informed perspectives, we aim to construct a hybrid approach for hydrodynamic lubrication classification and prediction, where data-driven methods are combined with physics-informed approaches to achieve a fast and accurate prediction of the hydrodynamic lubrication scenario. Our approach will spur the application of data mining methods in lubrication studies.

PENDEKATAN DATA MINING UNTUK MEMILIH PRODUK TERLARIS MENGGUNAKAN ALGORITMA NAIVE BAYES

Technological advances today can be exploited to process data into more useful information. In data collection, information collection is especially useful to maximize profits and develop marketing strategies. One way to increase profits is by using data mining techniques to help business actors in making decisions about stocks, increased profits and more. The Matahari Department Store is the largest retail platform in Indonesia, one of the retail stores located in Batam is the Matahari Department store Nagoya Hill Batam. The transaction data on the store that is still processed does not use a method that causes the processing of product sales data to be less effective and less efficient. Seeing from the number of transactions, a system is needed to predict the sale of the best-selling product as long as it can determine the correct stock for the products sold and can increase the profit, sale and purchase of the product. This research was conducted with the aim of applying data mining methods using the Naive Bayes Classifier algorithm to select the best-selling products in the outlet of the Matahari Nagoya Hill Batam Department Store. By using the collected sales data, the system is expected to increase profits steadily and avoid shortages of product stocks. Through analysis using the Naive Bayes Classifier method, the study achieved an accuracy of 67% and obtained a bag sales result to be the best-selling sale during January 2023 through March 2023 with a sales percentage of 20%.

Methodological Basis and Experience of Using Data Mining Methods in Trade

The article explores data mining methods that allow us to get helpful information from the data. The possibility of using these methods in practice in the financial sector was considered. Since financial activity is closely related to our social life, the use of data mining methods plays an essential role in the analysis and forecasting of the financial market in the modern era of big data. However, due to differences in the experience of researchers in different disciplines, it is not easy to use data mining methods when analyzing financial data. Therefore, creating a methodological basis for the practical application of data mining methods in the analysis of financial data is an urgent issue. The purpose of this article is to create a methodological basis for using data mining methods for efficient trading. When processing product data, a priori methods and visualization methods were used, and their implementation in practice was described. As a result, scenarios of computer applications were created as a sample of the practical implementation of the algorithms of these methods. Building a quantitative trading strategy requires first statistical analysis of the information in the market and then testing the quantitative model on the collected data. This study developed a quantitative trading system based on data mining methods. The primary development tool used is the Jupyter web platform, and three cores have been developed: quantitative data selection, strategy testing on data, time series analysis, and visualization. The developed system supports modules for making simple trading decisions.

Establishing a link between complex courtyard spaces and thermal comfort: A major advancement in evidence-based design

Amidst climate change, the importance of climate-adaptive design in architecture and landscape design has surged, particularly in residential courtyards, where optimizing the microclimate is paramount to residents' well-being. Traditional spatial indices, however, fall short in accurately characterizing complex courtyards and local spatial features. To overcome these limitations, this study introduces pixel-level spatial indicators that effectively overcome these constraints. These indicators are implemented using computational geometry algorithms such as Ray Tracing, Flood Fill, and A*, enabling simulation of various courtyard spatial indicator maps. We also utilize Graphics Processing Unit (GPU)-based rapid thermal comfort simulation technology to generate thermal comfort maps. By applying data mining methods such as Partial Least Squares Regression (PLSR), Pearson correlation, and Nearest-neighbor interpolation, we explore the relationships between spatial indicators and thermal comfort, ultimately identifying key indicators and determining the guiding thresholds and influencing trends corresponding to heat discomfort frequency. Six key indicators and th emerge: Building View Factor (BVF), indicating building coverage visibility (prefer above 0.11); Solar Beam Fraction (BEAM), illustrating Summer solstice sun shading condition (prefer below 0.78); Averaged View Factor (AVF), showing overall visibility (prefer below 0.40); Directional Sky View Factor (DSVF(W)), reflecting sky visibility in a specific orientation (prefer below 0.73); Tree View Factor (TVF), denoting tree coverage visibility (prefer above 0.18); and Plan Water Ratio (PWR), signifying water surface proportion (aim for below 0.44). These insights, integrated into design tools, contribute to evidence-based microclimate regulation strategies, thereby enhancing urban residents' thermal comfort and overall well-being.

PREPARATION OF DATA BY STATISTICAL METHODS FOR FORECASTING THE SUCCESS OF THE PROFESSIONAL ACTIVITY OF AN INFORMATION SECURITY SPECIALIST

An urgent problem in the field of information security – the provision of human resources in the field of information security in the Russian Federation is presented. One of the factors influencing this is the lack of the possibility of determining at the initial stage the optimal choice of the future direction of activity of graduates of higher educational institutions with specialization in this field. To solve this problem, it is proposed to use methods of data mining, machine learning and algorithms based on artificial neural networks. For the successful application of data mining methods, several factors should be taken into account: the data set must be large enough. Secondly; the data must be structured and analyzed to extract the most important information; the data analysis methods must be adequate and accurate in order to determine the purpose of the graduate with the highest accuracy. Taking into account these factors, the article suggests a way to form the initial data set and prepare it using statistical methods to predict the success of the professional activity of an information security specialist. The results of this work can be useful for higher educational institutions, specialists in the field of information security, heads of personnel departments who are interested in optimizing the process of selecting qualified employees in this field.

Identifying the Most Important Factors in Determining the Osteoporosis in Women Using Data Mining Techniques

Osteoporosis is one of the primary causes of disability and mortality in the elderly. If osteoporosis's significant features can be identified, the risk of developing this disease will be reduced. In recent years, data mining approaches have become a suitable tool for medical researchers. This study applied data mining methods to identify osteoporosis’s significant features. This study applied data from women having osteoporosis or osteopenia in the period 2011-2019 in the Osteoporosis Diagnosis Center, Isfahan, Iran. Data mining methods such as linear regression, naïve bayes, decision tree, support vector machine, random forest, and neural network were implemented on the dataset. This study consisted of 8258 patients’ information, of which 1482 had osteoporosis. The results showed that the support vector machine, decision tree, neural network are the best method based on accuracy, precision, and AUC measures. Six candidate features were age, weight, back pain, low activity, menopause date, and previous fracture. Support vector machine, decision tree, and neural network are the best candidate techniques for predicting osteoporosis. Thin older people are more at risk of osteoporosis than other people. Yet, people with middleweight and middle age are at lower risk of osteoporosis.

Liver Disease Classification Using the Elbow Method to Determine Optimal K in the K-Nearest Neighbor (K-NN) Algorithm

Diagnosing liver disease in the field of healthcare is not an easy task. However, by utilizing medical records as datasets and applying data mining methods such as K-Nearest Neighbor (K-NN), we can analyze and extract knowledge automatically. The K-NN method has proven to be more effective compared to other methods as it clusters new information by selecting the nearest neighbors based on the value of k. In this study, we employed the Elbow method to determine the optimal value of k by measuring the error rate. The test results revealed that the optimal value of k was found to be 4, with the lowest error rate. In the third test, we achieved a training accuracy of 80.5% and a testing accuracy of 78.9%. After fine-tuning the training data, we were able to improve the accuracy to 82.2% for training and 77.1% for testing. However, in the fourth test, we encountered overfitting issues due to data imbalance caused by inappropriate resampling, resulting in a model that was overly complex and prone to excessive noise.

Tax Fraud Reduction Using Analytics in an East European Country

Tax authorities face the challenge of effectively identifying companies that avoid paying taxes, which is not unique to European Union countries. Limited resources often constrain tax administrators, who traditionally rely on time-consuming and labour-intensive tax audit tools. As a result of this established practice, governments are losing a lot of tax revenue. The main objective of this study is to increase the efficiency of the detection of tax evasion by applying data mining methods in the East European country Lithuania, which has a rapidly developing economy, by applying data mining methods concerning affluence-related impacts. The study develops various models for segmentation, risk assessment, behavioral templates, and tax crime detection. Results show that the data mining technique can effectively detect tax evasion and extract hidden knowledge that can be used to reduce revenue losses resulting from tax evasion. This study’s methods, software, and findings can assist decision-makers, experts, and scientists in developing countries in predicting tax fraud detection.

A Systematic Review of Data Mining Studies in Parenting Research

This study aims to provide a comprehensive review of the application of data mining methods in parenting research and to explore the insights offered by data mining algorithms in this domain. In this study, 31 academic parenting research studies, which utilized data mining techniques based on empirical data, are finally selected and thoroughly. They were subsequently analysed, in terms of research inquiries, sources of datasets, and methodological approaches, as well as systematically categorized. Consequently, seven primary research directions in the field of parenting research using data mining methods were outlined. This review also delves into the sources of datasets in extant studies, and discussed the added value that data mining methods bring to the field of parenting research, pointing towards potential directions for future research.

A Data-driven Causality Modeling Framework: An Empirical Study of Modeling the Effect of Indoor Air Quality Perception on Students' Cognitive Performance

In the information age, a paradigm revolution in applied data mining methods has emerged in response to the data explosion in management science research, but this artificial intelligence-based automated data-driven modeling process creates a new "big relationship" problem, where a large number of correlations obfuscate the identification of true causal relationships. In this paper, we use a causality modeling framework that combines correlation modeling and causality pruning processes. First, symbolic regression is used to model white-box correlations of human intelligence, and then spurious correlations that do not conform to causal graph theory are pruned so that ultimately causal relationships and explicit candidate models describing these relationships can be found automatically. In an empirical research problem, the framework is compared with a traditional hypothesis construction-validation process, and the results are consistent between the two. The proposed framework implements a data-driven "correlation + causation" automatic modeling capability, which will greatly improve modeling efficiency and reliability.

Development and application of data mining method for synthetic aperture radar image ship inspection based on big data application technology

Synthetic aperture radar belongs to the radar signal working in microwave band, which has the characteristics of strong penetrating performance, large area imaging, all-weather and so on, and is widely used in the civil field and military field. Especially in the background of the information era, synthetic aperture radar imaging technology has developed to different degrees, and the resolution of synthetic aperture radar images has been significantly improved, which is highly valued by the target detection field, and the detection of naval targets through the reasonable use of synthetic aperture radar images has become an important application direction in the field of marine remote sensing. Based on this, this paper analyzes the characteristics of ship targets in SAR images, analyzes the differences between them and optical images from different aspects, and then concentrates on the most common statistical models and prediction methods of clutter analysis in SAR images, proposes the most reasonable way of clutter distribution simulation, and then uses the experimental way to accurately evaluate the complex sea clutter in SAR images, so as to propose the similar characteristics of the ship detection method‥

Data mining of the healthcare system based on the machine learning model developed in the Microsoft azure machine learning studio

This article presents data mining, which is based on the methods of mathematical statistics and machine learning, describes the features of applying regression analysis methods in the machine learning systems. The developed machine learning model includes the regression analysis modules based on the Bayesian linear, artificial neural network, decision tree, decision forest, and linear regressions. In the process of applying this machine learning model, using the mentioned algorithms, the corresponding regression models were constructed and their comparative analysis was performed, the results were analyzed. The results obtained indicate the feasibility of using data mining in the medical research using machine learning systems. The presented methods can serve as a basis for strategic development of a new directions of the medical data processing and decision-making in this field. We have identified the prospects for further research aimed at applying data mining methods to the healthcare system, namely, clustering, classification, anomaly detection.

Application of data mining methods in solving medical problems

Mathematical methods in medicine - the criteria for determining the status of research objects related to medicine and health problems, methods and algorithms for assessing, analyzing and forming a set of quantitative variables are analyzed. Because of the analysis, problems and tasks were identified that need to be addressed.

HIGHER EDUCATION DASHBOARD IMPLEMENTATION USING DATA MINING AND DATA WAREHOUSE: A REVIEW PAPER

Most of the activities including lecturer recruiting, advertising, pupil admittance, scholar registration, educational procedures, and alumni system, most higher education institutions now use integrated information systems. The higher education information system produces a lot of transactional information, and the quantity of data is growing every day, nonetheless the best way to utilize it is yet unknown. Each piece of data saved on a data storage medium is used for a certain purpose. A higher education institution, on the other hand, requires a comprehensive understanding of all data. In light of these circumstances, an analytical tool is required to excerpt statistics and uncover useful knowledge from large amounts of data. Such procedures necessitate a longer processing time and a more complicated procedure. The goal of this study was to create a data warehouse model and dashboard for an analytical tool, as well as to apply data mining techniques to higher education institutions. The study approach started with the creation of knowledge requirements, followed by the creation of an evolutional archetype to a data warehouse, the application of data mining methods, and the creation of a console for an investigative utilities. The end consequence was basically a prototype of data mining, data warehouse, and systematic utility for higher education institutions that would help them enhance their analytical and decision-making processes and therefore improve their performance.

Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis

This study presents a novel approach for discovering actionable knowledge and exploring data-based models from data recorded by household smart meters. The proposed framework is supported by a machine learning architecture based on the application of data mining methods and spatial analysis to extract temporal and spatial restricted clusters of characteristic monthly electricity load profiles. In addition, it uses these clusters to perform short-term load forecasting (1 week) using recurrent neural networks. The approach analyses a database with measurements of 1000 smart meters gathered during 4 years in Guayaquil, Ecuador. Results of the proposed methodology led us to obtain a precise and efficient stratification of typical consumption patterns and to extract neighbour information to improve the performance of residential energy consumption forecasting.

Discovery of new safety knowledge from mining large injury dataset in construction

Safety in construction continues to remain as the 3rd most serious occupational problem worldwide. Previous research mainly used questionnaire surveys or interviews to obtain “cross-sectional” data, which may be difficult to extract patterns and gain new knowledge for improving safety performance. This research aims to discover the characteristics, patterns, mechanisms and knowledge from mining a large injury dataset, and to develop strategies for mitigating safety risks, and reducing accidents and injuries so that to improve construction safety performance. A large set of injury and incident data recorded by construction companies over a period of 11 years was used in this research. Statistical analysis, visualisation analysis and association rule mining (ARM) methods were applied to mine and analyse this large dataset. “Injury time”, “injured body locations, organs and systems”, “age distribution of injured”, “causes, nature and relationships of injury, and injured body parts” “changing trends of incidents”, were analysed in depth to discover new safety knowledge. Based on the knowledge and mechanism discovered, this study proposed five strategies for improving construction safety performance covering safety skills, safety awareness, safety protection and emergency response. This research would be valuable to both researchers and practitioners. Researchers would benefit from gaining a deeper understanding of the characteristics of construction safety injuries, application of data mining methods, and relevant future research directions, while practitioners would benefit by learning about the strategies proposed to mitigate injury risks and prevent incidents.

Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing an important role in the modern Information Technology (IT) revolution. Blockchain has recently been popularized by the rise of cryptocurrencies, while data mining has already been present in IT for many decades. Data stored in a blockchain can also be considered to be big data, whereas data mining methods can be applied to extract knowledge hidden in the blockchain. In a nutshell, this paper presents the interplay of these two research areas. In this paper, we surveyed approaches for the data mining of blockchain data, yet show several real-world applications. Special attention was paid to anomaly detection and fraud detection, which were identified as the most prolific applications of applying data mining methods on blockchain data. The paper concludes with challenges for future investigations of this research area.

A Survey on Data Mining Techniques for COVID Prediction

Corona Virus Disease of 2019 (COVID-19) has emerged as a serious health emergency worldwide. The symptoms of COVID-19 are un-detectable at early stage in most of the patients. It spreads from person to person very rapidly and causes severe sickness and loss of life in a number of cases if not treated early. Data mining techniques are very commonly being used in medical sector for detection and prediction of a variety of diseases and medical conditions of patients. A number of researchers are also working towards prediction of possibility of infection of COVID-19 among humans using machine learning techniques, specifically by applying data mining methods. In this paper, an extensive survey of available literature in the domain of prediction of COVID-19 infection and other diseases has been presented. This also includes survey on data mining techniques, models and various datasets.