Data Mining Applications Research Articles

APLIKASI MARKET BASE ANALITIK UNTUK MEMPELAJARI POLA BELANJA MASYARAKAT PADA TOKO UD GARAM KUSTONO MENGGUNAKAN ALGORITMA APRIORI

At this time the transaction data at the UD Garam Kustono store has decreased, this shows that thereis an increase in sales competition followed by an increase in applying information technology. Toovercome these problems, a very appropriate sales strategy is needed to use the availability of usefuldata and information. In order for data from sales transactions to be more efficient, it is necessary toprocess data by applying data mining techniques. The method used by researchers is the a priorialgorithm. The aim of this study is to apply a data mining system to find information oninterrelationships between products that can support sales strategies from transaction data. Thisinformation will be used to support sales strategy decisions by adjusting the products to be offeredto buyers. The results of this study will show that this a priori algorithm is used to process salestransaction data and become information in the form of links between products that are most oftenpurchased by the public.

Innovations in Healthcare Analytics: A Review of Data Mining Techniques

This review article provides an overview of the current state of data mining applications in healthcare, including case studies, challenges, and future directions. The article begins with a discussion of the role of data mining in healthcare, highlighting its potential to transform healthcare delivery and research. It then provides a comprehensive review of the various data mining techniques and tools that are commonly used in healthcare, including predictive modelling, clustering, and association rule mining. The article also discusses some key challenges associated with data mining in healthcare, such as data quality, privacy, and security, and suggests possible solutions. Finally, the article concludes with a discussion of the future directions of data mining in healthcare, highlighting the need for continued research and development in this field. The article emphasises the importance of collaboration between healthcare providers, data scientists, and policymakers to ensure that data mining is used ethically and effectively to improve patient outcomes and support evidence-based decision-making in healthcare.

Analyzing the Impact of Epidermoid and Adeno Tissue on Cancer Incidence Whit a Data Mining Approach

Early detection is the only way to effectively control diseases whose treatment can be challenging, expensive, and time-consuming. Identifying the influencing factors in the occurrence of disease can, therefore, reduce the time associated with diagnosis and provide a solid foundation for improving the prognosis and preventing patients' deterioration. Applying data mining techniques as a novel approach to the early detection of disease-causing agents can significantly assist the early detection. In this study, an attempt was made to investigate the effect of epidermoid and adeno tissues on the incidence of cancerous diseases such as bone, bone marrow, lung, and neck cancer by conducting a data mining process on cancer patient data sets. Hence, Implementing two data mining techniques, K-nearest neighbor and decision tree, on the data of patients with these four types of cancer, an attempt was made to evaluate their performance using the three criteria of accuracy, error ratio, and negative prediction value. The implementation of data mining techniques and evaluations of their performance indicates that the decision tree technique performed better with an accuracy of 89.10%, an error ratio of 14.04%, and negative prediction value of 77.71%. Also, based on the findings, contamination of epidermoid and adeno tissues does not affect the early detection of any of the four categories of bone, bone marrow, lung, or neck cancer. In other words, the infection of the two epidermoid and adeno tissues cannot be the cause of the four types of bone, bone marrow, lung, and neck cancer

ModEx: a general purpose computer model exploration system.

We present a general purpose visual analysis system that can be used for exploring parameters of a variety of computer models. Our proposed system offers key components of a visual parameter analysis framework including parameter sampling, deriving output summaries, and an exploration interface. It also provides an API for rapid development of parameter space exploration solutions as well as the flexibility to support custom workflows for different application domains. We evaluate the effectiveness of our system by demonstrating it in three domains: data mining, machine learning and specific application in bioinformatics.

Review Paper Data Mining Klasifikasi Data Mining

The process of combining statistical techniques, mathematical calculations, Artificial Intelligence (AI) and machine learning to extract useful and interrelated information from large amounts of data. Data mining is commonly used to analyze and explore big data to get useful information. There is a lot of information that can be extracted from processing using data mining, such as analyzing consumer purchases or making decisions regarding the production process. Science has often been implemented to solve problems that arise from existing circumstances. In addition to the knowledge needed in order to solve problems or make strategic decisions in dealing with problems that arise, past experience or data that has been obtained at the time of the lights can also be used as a reference for making decisions. Data mining is the process of extracting (mining) information from a set of past data which is then displayed as knowledge to be used in accordance with the desired needs. Research on data mining has been carried out by many researchers to date related to the application of data mining to solve the problems they face, even though the problems are different in type and designation. This paper was written to review existing papers regarding data mining, especially classification. And to get information and map from research that has been done to be used as literature on the author's research plan. Through this review, it is hoped that later you can choose the right method to process data using data mining for the best results.

Data analysis with performance and privacy enhanced classification

Abstract Privacy is the main concern in cyberspace because, every single click of a user on Internet is recognized and analyzed for different purposes like credit card purchase records, healthcare records, business, personalized shopping store experience to the user, deciding marketing strategy, and the list goes on. Here, the user’s personal information is considered a risk process. Though data mining applications focus on statistically useful patterns and not on the personal data of individuals, there is a threat of unrestricted access to individual records. Also, it is necessary to maintain the secrecy of data while retaining the accuracy of data classification and quality as well. For real-time applications, the data analytics carried out should be time efficient. Here, the proposed Convolution-based Privacy Preserving Algorithm (C-PPA) transforms the input into lower dimensions while preserving privacy which leads to better mining accuracy. The proposed algorithm is evaluated over different privacy-preserving metrics like accuracy, precision, recall, and F1-measure. Simulations carried out show that the average increment in the accuracy of C-PPA is 14.15 for Convolutional Neural Network (CNN) classifier when compared with results without C-PPA. Overlap-add C-PPA is proposed for parallel processing which is based on overlap-add convolution. It shows an average accuracy increment of 12.49 for CNN. The analytics show that the algorithm benefits regarding privacy preservation, data utility, and performance. Since the algorithm works on lowering the dimensions of data, the communication cost over the Internet is also reduced.

RETRACTED ARTICLE: Application of data mining based on swarm intelligence algorithm in financial support of livestock and poultry breeding insurance

Among the components of China’s actual economic structure, aquaculture has always been a key project, and as a part of agriculture, it has been greatly developed. However, there are still many problems in the actual breeding process, such as low financial support, low information transparency, high financing difficulty and lack of channels, high claims costs and serious moral hazard in the livestock and poultry breeding insurance field, which will affect the development and progress of the industry. Therefore, in this context, this paper improves the efficiency analysis of financial support for livestock and poultry breeding insurance by introducing data mining and swarm intelligence algorithms. Then use the analysis data to build a model to further evaluate the efficiency of financial support. The construction of the model is roughly divided into four parts: insurance density, insurance depth, insurance amount and income. The weight of different indicators is calculated through the analytic hierarchy process, and the overall ranking results are output through consistency test. Some data of the above models are used as the output indicators of DEA decision-making units for top-down ranking. The result analysis shows that the development of technology in this industry can effectively improve the actual productivity of agricultural crops, thus effectively promoting the development of local agriculture. In this paper, data mining and swarm intelligence algorithms are introduced into livestock and poultry breeding insurance to complete the analysis and evaluation of financial support.

Intelligent engineering management modeling for computer technology and data mining

The author suggests using computer technology and data mining in intelligent engineering management to increase the management capacity of engineering projects and decrease the consumption of construction expenses. The emphasis is mostly on theoretical and analytical approaches to problems of practical concern for data mining, perhaps in combination with other conventional tools, and the associated applications to engineers and managers of various industrial sectors. This has immediate benefits for both academic and applied data mining researchers as well as research students. The author developed a BIM project management system based on Browser/Server architecture and Client/Server architecture in conjunction with BIM technology and the BIM 4D model. To execute complete project management of business management, real-time control, and decision support, the system must establish and use its business logic and data exchange link. Each module in the 4D construction dynamic management subsystem recognizes dynamic management of the construction process, and the working duration of each module is determined by calculating by using the unified engineering decomposition principle, rationalizing the relevant data through time parameters, recording the acquired management data and storing it in the BIM database, and connecting the BIM database bi-directionally through the web server through the system integration. The results showed that when the time is 20[Formula: see text]min, the throughput of the system is higher than [Formula: see text][Formula: see text]kb/s. The system can effectively improve the project management ability, reduce the construction cost and construction period of the project, and the system responds quickly.

RETRACTED ARTICLE: Application of data mining in enterprise financial risk prediction based on genetic algorithm and linear adaptive optimization

In the construction of enterprise financial risk prediction model, the obtaining of the optimal solution has a key impact on the prediction effect of the prediction model. Exploring the optimization and improvement of the prediction model to obtain the optimal solution is an urgent problem. In this paper, based on genetic algorithm and data mining technology, a genetic algorithm model based on adaptive improvement is proposed. Through linear adaptive optimization, the optimal solution of complex problems is obtained. When estimating parameters, EM algorithm is used to estimate, so that the final effect of model fusion can reach the expectation. At the same time, it integrates data mining technology, Borderline STATE EasyEnsemble sampling method and SVM intelligent technology to build an optimized comprehensive risk early warning model, which realizes the automatic integration and prediction of financial risks. The adjusted mathematical model is better than the single technical model. This study verified that the prediction ability of the improved model for minority samples has been greatly improved. Under the new method, F1 + F2 + F3 has increased by 14.83% compared with the single F1 feature, and the prediction accuracy of most types of samples has not been reduced. The prediction effect has generally improved. Finally, according to the current situation of enterprise finance, this paper puts forward effective measures to prevent enterprise financial risks.

Biogeography-based optimization for feature selection

Data clustering has many applications in medical sciences, banking, and data mining. K-means is the most popular data clustering algorithm due to its efficiency and simplicity of implementation. However, K-means has some limitations, which may affect its effectiveness, such as all the features having the same degree of importance. To address these limitations and improve K-means accuracy, we adopt the Biogeography-Based Optimization (BBO) algorithm to select the most relevant features of datasets. Our primary idea is to reduce the intra-cluster distance while increasing the distance between clusters.

Data-Driven Approach for Credit Risk Analysis Using C4.5 Algorithm

Data-Driven Approach for Credit Risk Analysis Using C4.5 Algorithm

Prediction of the mechanical behavior of mortars incorporating phase change materials using data mining techniques

Nowadays, it is imperative to reduce the energy bill in order to contribute to a more sustainable planet. In this sense, the use of materials that contribute to the energy efficiency of buildings is a very important contribution to achieve this goal. Mortars incorporating phase change materials (PCM) can make an important contribution to this end, due to its thermal storage capacity, increasing the energy efficiency of buildings. In this work several mortars with different PCM contents were developed, using different binders (cement, aerial lime, hydraulic lime and gypsum). The aim of this study was to apply data mining techniques such as artificial neural networks (ANN), support vector machines (SVM) and multiple linear regressions (MLR) to forecast the compressive and flexural strengths of these mortars at different exposure temperatures. It was concluded that ANN models have the best predictive capacity both for compressive strength and flexural strength. However, the SVM models have a flexural strength forecasting capacity very close to ANN models.

RD-FCA: A resilient distributed framework for formal concept analysis

Formal Concept Analysis (FCA) is a data analysis technique with applications in data mining, artificial intelligence, software engineering, etc. The algorithms for FCA are computationally expensive, and their recursion tree is highly irregular and dynamic in nature. Several distributed FCA algorithms have been proposed to exploit parallelism within and across machines. However, none of the distributed approaches are able to recover from failures in the system. We propose RD-FCA, the first resilient distributed framework for FCA that uses novel load-balancing strategy, handles fail-stop failures and provides at-least-once semantics for concept discovery. Our asynchronous snapshot mechanism with incremental updates reduces the snapshot overhead and minimizes recalculation of concepts during recovery. RD-FCA also supports dynamic addition of workers to accelerate performance. Compared to MapReduce based approaches, RD-FCA performs an order of magnitude faster. We show through extensive evaluation that RD-FCA recovers efficiently from single, multiple, independent and cascading failures.

Using data mining technology to explore causes of inaccurate reliability data and suggestions for maintenance management

Reliability data reflects equipment safety and provides a reference for setting inspection period, thereby serving as crucial information for the implementation of equipment integrity management policies. The calculation foundation of reliability data is maintenance records of adequate data quality. However, maintenance records of doubtful quality are common. Despite excluding poor quality recodes and using only the remaining maintenance recodes to calculate the reliability data, the calculated results generally lack a sufficient degree of confidence. This study applied data mining technology, including quality metrics, the association rule, and clustering, to explore the cause of low-quality maintenance data. The results revealed that the low data quality of maintenance records was due to ineffective maintenance policies, the low integrity of key system columns, nonadherence to the policy, and misunderstanding of column definitions. The proposed method successfully identified the causes of low-quality maintenance records. By incorporating the method into the function module of a CMMS, operators can equip the system with self-diagnosis, self-supervision, and continuous optimization functions.

Examining transaction-specific satisfaction and trust in Airbnb and hotels. An application of BERTopic and Zero-shot text classification

With a methodological approach, this article explores the application of data mining to the user-generated content of tourist accommodation on infomediation platforms and social networks. Its objective is to present an algorithm that allows the identification of service characteristics relevant to guest satisfaction and trust. Our study processes unstructured, natural-language data about Airbnb and hotel stays (the final dataset was 12,236 Airbnb sentences and 12,200 hotel sentences from 2018 until September 25 2021). Among the results is a computational algorithm that uses BERTopic to identify latent themes (or topics) in the narratives. Secondly, our analysis applies a Zero-shot classification approach for classifying guest reviews into labels related to guests' satisfaction and trust. Thirdly, we execute a Principal Component Analysis to investigate the sufficiency relationships between extracted topics, customer satisfaction, and trust-based labels. To sum up, and as practical implications, our study adds to the knowledge about the sharing economy by providing insights for developing marketing policies and a better understanding of hospitality services.

Design and Management of Micro-Teaching Mode of Education in Colleges Driven by Big Data

Big data has become a new driving force for national innovation and entrepreneurship. Although colleges and universities, which are responsible for cultivating high-quality entrepreneurship and innovation talents, have achieved certain results, there are still many problems in practice, which need to be driven by big data. Big data and the practice of innovation and entrepreneurship education in colleges and universities have certain inherent commonality. The integration of big data into the practice of innovation and entrepreneurship education in colleges and universities needs to be improved and strengthened in terms of top-level design, data environment, and educational concepts. Educational big data has become a hot topic and trend in educational research. College students’ entrepreneurship is a kind of entrepreneurial process in which special groups of college students and graduate students are the main body of entrepreneurship. With the recent transformation process of our country and the increasing pressure of social employment, entrepreneurship has gradually become a career choice for college students and graduates. Through data mining, statistical analysis, model construction, and comprehensive reasoning of educational big data are realized, and positive suggestions and countermeasures are provided for education and teaching. On the basis of the pattern analysis of educational data mining process, the concrete application of educational data mining is analyzed, and the correlation and rules between educational phenomena are found, so as to provide educational prediction and educational decision support for further optimization of teaching.

Research on the application of data mining in the quality monitoring of talent training in private universities

How to achieve the coordinated development of education scale and talent training quality is closely related to the revitalization of the nation and the future of the country, and is also the only way for private universities to survive and seek development. According to the survey and statistics, the students of private universities have relatively weak independent learning ability and lack of self-supervision. Therefore, in addition to the reform of talent training mode, private undergraduate universities also urgently need to improve the corresponding talent training quality monitoring system. This paper uses data mining technology and statistical analysis methods to carry out relevant research on the quality monitoring of talent cultivation in private universities at this stage. The research results can provide reference for improving the monitoring and evaluation management of talent cultivation quality in private universities.

Application of data mining in female sports behavior prediction based on FCM algorithm

Application of data mining in female sports behavior prediction based on FCM algorithm

Handling Big Data in Education: A Review of Educational Data Mining Techniques for Specific Educational Problems

In the era of big data, where the amount of information is growing exponentially, the importance of data mining has never been greater. Educational institutions today collect and store vast amounts of data, such as student enrollment and attendance records, and their exam results. With the need to sift through enormous amounts of data and present it in a way that anyone can understand, educational institutions are at the forefront of this trend, and this calls for a more sophisticated set of algorithms. Data mining in education was born as a response to this problem. Traditional data mining methods cannot be directly applied to educational problems because of the special purpose and function they serve. Defining at-risk students, identifying priority learning requirements for varied groups of students, increasing graduation rates, monitoring institutional performance efficiently, managing campus resources, and optimizing curriculum renewal are just a few of the applications of educational data mining. This paper reviews methodologies used as knowledge extractors to tackle specific education challenges from large data sets of higher education institutions to the benefit of all educational stakeholders.

Concepts and applications of data mining and analysis of social networks

Social media has become an important reference for information during the last few decades. They have been able to be effective in various fields such as business, entertainment, science, crisis management, politics, etc. For this reason, a social media analysis has become very important for researchers and large companies. The widespread use of social media leads to a complex problem called "accumulation of data". Many data science specialists seek to analyze this data in order to identify the behavioral characteristics of users, analyze interests and needs, and improve marketing processes. Different social media platforms have the ability to use all kinds of media, including text data, video, video, audio, and location information, etc. Therefore, data analysis in social networks is very important. In this research, the concepts and applications of data analysis in social networks will be investigated.