Knowledge Discovery In Databases Process Research Articles

Refining the Knowledge Discovery Process: Introducing a Data Management Phase for Ethical and Efficient Post- Analysis Data Handling

In the evolving landscape of Knowledge Discovery in Databases (KDD), the efficient management of data post-discovery emerges as a pivotal yet often overlooked consideration. This research proposes an innovative extension to the conventional KDD process by introducing a "Data Management" focused on the crucial decision-making regarding data deletion or data compression after knowledge has been extracted. This addition aims to address the multifaceted challenges of ethical considerations, security vulnerabilities, and storage inefficiencies that accompany large datasets in the post-analysis phase. Through a comprehensive exploration of the KDD process, including an analysis of existing methodologies and the integration of our proposed step, we elucidate the significant impact of data management on enhancing the ethical, secure, and efficient utilization of database information. Our findings highlight the necessity for a standardized approach to data retention or reduction, providing clear guidelines that balance organizational needs with legal and ethical standards. This paper not only contributes to the academic discourse on KDD process optimization but also offers practical insights for organizations striving to navigate the complexities of data management in an ethically responsible and resource-efficient manner.

Identification of high-risk beneficiaries in private healthcare insurance.

The objective of this study was to apply the Knowledge Discovery in Databases process to find out if beneficiaries of a private healthcare insurance would belong, at least once, to the 'very high cost' and 'complex cases' groups throughout the 12 months after the month when algorithms were applied. Datasets were built containing information on beneficiaries' effective use of their health plan, as well as their characteristics. Five machine learning algorithms were used, namely Random forest, Extra tree, Xgboost, Naive bayes and K-nearest neighbor. The K-nearest neighbor algorithm had a recall rate of 81.12%, 83.77% precision and an Area Under the Curve (AUC) value of 0.9045. The study also revealed that categorization occurs, on average, 8.11 months before a beneficiary entering, for the first time, a high-risk group, considering the dataset classification from January 2019 to June 2020.

SMOTE vs. KNNOR: An evaluation of oversampling techniques in machine learning

The increasing availability of big data has led to the development of applications that make human life easier. In order to process this data correctly, it is necessary to extract useful and valid information from large data warehouses through a knowledge discovery process in databases (KDD). Data mining is an important part of this and it involves discovering data and developing models that extract unknown patterns. The quality of the data used in supervised machine learning algorithms plays a significant role in determining the success of predictions. One factor that improves the quality of data is a balanced dataset, where the input values are distributed close to each other. However, in practice, many datasets are unbalanced. To overcome this problem, oversampling techniques are used to generate synthetic data that is as close to real data as possible. In this study, we compared the performance of two oversampling techniques, SMOTE and KNNOR, on a variety of datasets using different machine learning algorithms. Our results showed that the use of SMOTE and KNNOR did not always improve the accuracy of the model. In fact, on many datasets, these techniques resulted in a decrease in accuracy. However, on certain datasets, both SMOTE and KNNOR were able to increase the accuracy of the model. Our results indicate that the effectiveness of oversampling techniques varies depending on the specific dataset and machine learning algorithm being used. Therefore, it is crucial to assess the effectiveness of these methods on a case-by-case basis to determine the best approach for a given dataset and algorithm.

Komparasi Metode Apriori dan FP-Growth Data Mining Untuk Mengetahui Pola Penjualan

Sales data is generally still rarely used, as well as the Perfume Corner shop just piling up in the database, even though there are problems experienced by the store regarding sales data for the best-selling products and to increase the number of sales of subsequent perfume products, so that the store can survive and develop even better. The algorithm that can be used to manage sales data to overcome this problem is Apriori. The research method used in this research is the KDD (Knowledge Discovery in Database) process. This research produces a high frequency pattern for itemsets with a minimum support value of 20% resulting in products that become The Most Tree Items namely Jo Malone 82.49%, Zarra 28.25%, and Zwitsal 20.34%. While the association rules formed from the value of Min. Supp 20% and Min. Conf 80%, get a combination of 2 itemsets, namely Jo Malone and Zarra. Whereas for the combination of 3 itemsets, namely Jo Malone, Zarra and Baccarte with valid and strong status, it is proven by a lift value greater than 1, therefore the association rules are very appropriate to be used.

Predicting Job Change among Data Scientists using Machine Learning Technique

In the knowledge and data-driven economy, countless ramifications were attributed to great contribution of data scientists in transforming business and industries by using various data science tools in recognizing and generating patterns in data points to generate insights. The study aimed at applying data science in human resources, and generates actionable intelligence, and HR analytics to better understand employees' perception towards the company, work environment. The researcher used the processes of Knowledge Discovery in Databases (KDD) method. Knowledge discovery in databases is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns or relationships within a dataset (10,000 examples, 0 special attributes, and 14 regular attributes) to make important decisions. RapidMiner was used perform the KDD processes of selecting, pre-processing, data transformation, data mining using machine learning algorithm. Accordingly, Decision Tree was found to be the learning algorithm fit for the ExampleSet. Further, among 14 attributes, the most important attribute to split on was the city_development_index. This implies that the best predictor variable for job change among data scientists was the city_development_index. Consequently, the prediction model has 92.1% confidence that a Male who works in a city with a development index of 0.920, with relevant data science experience, not presently enrolled in the university, high school graduate, with 5 years of work experience, presently working in a Funded Start-up company with 50-99 employees, works for the first time with training hours=24 was predicted will "Not Change" a job. The model has 77.78% accuracy, and 81.70% precision. Keywords: Data Scientist, Data Science, Job Change, Human Resource Analytics

Knowledge Discovery in Health Domain using Deep Neural Network Algorithms

Knowledge discovery in databases (KDD) primarily depends on finding a strategy for effectively processing data. Data mining is a critical phase in the KDD process for extracting a valuable pattern from a dataset. Pattern extraction and discovery are complex processes that often require a vast dataset. Several applications and systems are used for health care and clinical data to diagnose and record patient records. The installed systems' primary goal is to extract a relevant pattern capable of improving healthcare services. To improve healthcare services, data mining necessitates the right design and execution of data mining algorithms to detect a unique pattern from large amounts of data. As a result, we propose using patient information from the Hewa Hospital in Sulamani, which is in charge of cancer and blood diseases, as a case study for our research. The primary goal of this research is to look at deep neural networks (DNN) and artificial neural networks (ANN) as classification algorithms that can assist us in making better judgments. The results show that the DNN algorithm outperforms the ANN method. When utilizing a 70:30 training and testing dataset, the score can reach 87.84.

How to undertake reviews of large collections of articles and establish main contributions: an ontology-based literature review approach

Identifying the main contributions of scientific works may demand time and previous depth knowledge, besides it can be a bottleneck for reviewing processes. To make it easier, this paper presents a standard representation based on the Knowledge Discovery in Databases process to characterize knowledge-discovery-in-databases scientific documents. This first attempt consists of a string composed of five elements, which provide: (i) the complexity of the problem; the tasks from the (ii) preprocessing, (iii) processing, and (iv) post-processing step, and (v) the auxiliary approaches such document used to achieve its objectives. As all documents can be classified using this approach, this proposal is scalable by text mining algorithms, making the review working easier for scientists since this methodology saves the time of the document's main classification.

Diabetes Prediction: A Study of Various Classification based Data Mining Techniques

Data Mining is an integral part of KDD (Knowledge Discovery in Databases) process. It deals with discovering unknown patterns and knowledge hidden in data. Classification is a pivotal data mining technique with a very wide range of applications. Now a day’s diabetic has become a major disease which has almost crippled people across the globe. It is a medical condition that causes the metabolism to become dysfunctional and increases the blood sugar level in the body and it becomes a major concern for medical practitioner and people at large. An early diagnosis is the starting point for living well with diabetes. Classification Analysis on diabetic dataset is a part of this diagnosis process which can help to detect a diabetic patient from non-diabetic. In this paper classification algorithms are applied on the Pima Indian Diabetic Database which is collected from UCI Machine Learning Laboratory. Various classification algorithms which are Naïve Bayes Classifier, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Support Vector Classifier and XGBoost Classifier are analyzed and compared based on the accuracy delivered by the models.

The implementation of the ARIMA-ARCH model using data mining for forecasting rainfall in Bandung city

A time series is a stochastic process which is arranged by time simultaneously. In this article, a time series model is used in accordance with Box-Jenkins' procedure. The Box-Jenkins procedure consists in identifying the model, estimating the parameters and diagnostic checking. The time series model is differentiated according to the number of variables, i.e. univariate and multivariate. The univariate method for the time series model that is often used is the Autoregressive Integrated Moving Average (ARIMA) model and the multivariate time series model is the Vector Autoregressive Integrated Moving Average (VARIMA) model. In this research, we studied the ARIMA model which is studied with a non-constant error variance. In this case, the Autoregressive Conditional Heteroscedasticity (ARCH) model is applied to outgrow the non-constant error variance. Selection of the best model by examining the minimum AIC for each model. The ARIMA-ARCH model is implemented on rainfall data in Bandung city with Knowledge Discovery in Database (KDD) in Data Mining. The methodology in the KDD process, including pre-processing, data mining process, and post-processing. Based on the results of model fitting, the best model is the ARIMA (2,1,4)-ARCH (1) model. The result of forecasting rainfall in Bandung shows a MAPE value is 11%, which has a similar pattern with actual data for short time 2-4 days. From these results, we conclude that the ARIMA-ARCH model is a good model for forecasting the rainfall in Bandung city.

Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

Industry 4.0 has remarkably transformed many industries. Supervisory control and data acquisition (SCADA) architecture is important to enable an intelligent and connected manufacturing factory. SCADA is extensively used in many Internet of Things (IoT) applications, including data analytics and data visualization. Product quality management is important across most manufacturing industries. In this study, we extensively used SCADA to develop a cloud-based analytics module for production quality predictive maintenance (PdM) in Industry 4.0, thus targeting textile manufacturing processes. The proposed module incorporates a complete knowledge discovery in database process. Machine learning algorithms were employed to analyze preprocessed data and provide predictive suggestions for production quality management. Equipment data were analyzed using the proposed system with an average mean-squared error of ~0.0005. The trained module was implemented as an application programming interface for use in IoT applications and third-party systems. This study provides a basis for improving production quality by predicting optimized equipment settings in manufacturing processes in the textile industry.

Optimalisasi Pelayanan Perpustakaan terhadap Minat Baca Menggunakan Metode K-Means Clustering

Knowledge Discovery in Database (KDD) is a structured analysis process aimed at getting new and correct information, finding patterns from complex data, and being useful. Data mining is at the core of the KDD process. Clustering is a data mining method that is suitable for optimizing library services because it can cluster books effectively and efficiently, with the K-Means algorithm data can be clustered and information from each centroid value of each cluster. Library services can optimize the placement of books so that students can quickly find books according to their reading interest more effectively and can be attracted to other books because they are in one grouping. Meanwhile, the library can prioritize the procurement of the next book. Optimization of library services in the cluster using the K-Means method. Clustering interest in reading has the criteria for the number of books available, borrowed books, and the length of time the books are borrowed. The book data is clustered into 3, namely very interested, in demand, and less desirable. After doing the calculation process from 40 samples of book types, it resulted in 6 iterations, and the final results were 3 clustering, namely cluster 1 of 4 books that were of great interest, cluster 2 of 20 books that were of interest, and cluster 3 of 16 books that were less desirable. This research can be used as a recommendation reference for optimizing library services both for the layout and procurement of books by prioritizing the types of books that are of great interest.

Evaluating the Effective Factors of Hospital Rooms on Patients' Recovery Using the Data Mining Method.

This study aims to investigate the effective environmental factors of hospital rooms in patients' recovery through data mining techniques. Previous studies have shown the positive effect of the interior environment of the hospitals on patients' recovery. The methods of these studies were mainly based on the evidence and patients' perception while hospital environments are associated with a large amount of data that make them an appropriate case for data mining studies. But data mining studies in hospitals mainly focused on medical and management purposes rather than evaluating the interior environment condition. We analyzed the hospital information system data of a hospital using Python programming language and some of its libraries. Preprocessing and eliminating the outliers, labeling and clustering of diseases, data visualization and analysis, final evaluation, and concluding were done using the knowledge discovery in databases process. Pearson coefficient value for rooms' area was .5 and, respectively, for the distance from the ward entrance and nursing station were .75 and .70. The χ2 values for the variables of room types, location, and occupation were 24.62, 18.98, and 21.53, respectively, and for the beds' location was 0.12. The results confirmed the correlation of the length of stay with the room types, location, and occupation, distance from the nursing station and ward entrance and also showed a moderate correlation with the rooms' area. However, no evidence was found about the relationship between the beds' location in rooms and patients' length of hospital stay.

EEPSA as a core ontology for energy efficiency and thermal comfort in buildings

Achieving a comfortable thermal situation within buildings with an efficient use of energy remains still an open challenge for most buildings. In this regard, IoT (Internet of Things) and KDD (Knowledge Discovery in Databases) processes may be combined to address these problems, even though data analysts may feel overwhelmed by heterogeneity and volume of the data to be considered. Data analysts could benefit from an application assistant that supports them throughout the KDD process and aids them to discover which are the relevant variables for the matter at hand, or informing about relationships among relevant data. In this article, the EEPSA (Energy Efficiency Prediction Semantic Assistant) ontology which supports such an assistant is presented. The ontology is developed on the basis that a proper axiomatization shapes the set of admitted models better, and therefore, establishes the ground for a better interoperability. On the contrary, underspecification facilitates the admission of non-isomorphic models to represent the same state which hampers interoperability. This ontology is developed on top of three ODPs (Ontology Design Patterns) which include proper axioms in order to improve precedent proposals to represent features of interest and their respective qualities, as well as observations and actuations, the sensors and actuators that generate them, and the procedures used. Moreover, the ontology introduces six domain ontology modules integrated with the ODPs in such a manner that a methodical customization is facilitated.

Uma análise do acervo da revista produção online

Para a realização de meta-análises do acervo da Revista Produção Online foram considerados o processo de Descoberta de Conhecimento em Bases de Dados e conceitos de Teoria dos Grafos. Os resultados obtidos possuem estatísticas gerais, análises das redes de colaboração, termos em destaque por meio do uso de mineração de texto e a identificação de autores considerados influentes, com base em frequência de publicação, e nas medidas de centralidade em grafos. O objetivo do trabalho é apresentar um panorama geral e de contribuições da revista, de maneira impessoal, apolítica e restrita a dados públicos. Os resultados obtidos apoiam interpretações, perspectivas e saberes deste importante acervo.

Survey of Network Intrusion Detection Methods From the Perspective of the Knowledge Discovery in Databases Process

The identification of cyberattacks which target information and communication systems has been a focus of the research community for years. Network intrusion detection is a complex problem which presents a diverse number of challenges. Many attacks currently remain undetected, while newer ones emerge due to the proliferation of connected devices and the evolution of communication technology. In this survey, we review the methods that have been applied to network data with the purpose of developing an intrusion detector, but contrary to previous reviews in the area, we analyze them from the perspective of the Knowledge Discovery in Databases (KDD) process. As such, we discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. In addition, we also present the characteristics and motivations behind the use of each of these techniques and propose more adequate and up-to-date taxonomies and definitions for intrusion detectors based on the terminology used in the area of data mining and KDD. Special importance is given to the evaluation procedures followed to assess the different detectors, discussing their applicability in current real networks. Finally, as a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.

A review of data mining in knowledge management: applications/findings for transportation of small and medium enterprises

A core subfield of knowledge management (KM) and data mining (DM) constitutes an integral part of the knowledge discovery in database process. With the explosion of information in the new digital age, research studies in the DM and KM continue to heighten up in the business organisations, especially so, for the small and medium enterprises (SMEs). DM is crucial in supporting the KM application as it processes the data to useful knowledge and KM role next, is to manage these knowledge assets within the organisation systematically. At the comprehensive appraisal of the large enterprise in the transportation sector and the SMEs across various industries—it was gathered that there is limited research case study conducted on the application of DM–KM on the transportation SMEs in specific. From the extensive review of the case studies, it was uncovered that majority of the organisations are not leveraging on the use of tacit knowledge and that the SMEs are adopting a more traditional use of ICTs to its KM approach. In addition, despite DM–KM is being widely implemented—the case studies analysis reveals that there is a limitation in the presence of an integrated DM–KM assessment to evaluate the outcome of the DM–KM application. This paper concludes that there is a critical need for a novel DM–KM assessment plan template to evaluate and ensure that the knowledge created and implemented are usable and relevant, specifically for the SMEs in the transportation sector. Therefore, this research paper aims to carry out an in-depth review of data mining in knowledge management for SMEs in the transportation industry.

Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

Nowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

A review of knowledge discovery process in control and mitigation of avian influenza.

In the last several decades, avian influenza virus has caused numerous outbreaks around the world. These outbreaks pose a significant threat to the poultry industry and also to public health. When an avian influenza (AI) outbreak occurs, it is critical to make informed decisions about the potential risks, impact, and control measures. To this end, many modeling approaches have been proposed to acquire knowledge from different sources of data and perspectives to enhance decision making. Although some of these approaches have shown to be effective, they do not follow the process of knowledge discovery in databases (KDD). KDD is an iterative process, consisting of five steps, that aims at extracting unknown and useful information from the data. The present review attempts to survey AI modeling methods in the context of KDD process. We first divide the modeling techniques used in AI into two main categories: data-intensive modeling and small-data modeling. We then investigate the existing gaps in the literature and suggest several potential directions and techniques for future studies. Overall, this review provides insights into the control of AI in terms of the risk of introduction and spread of the virus.

A practical, iterative framework for secondary data analysis in educational research

Secondary data analysis in educational research has been an established research method for many years. Yet, few publications outline the “how to” of undertaking the process. This paper presents an analysis framework suitable for undertaking secondary data analysis within the field of education. The framework is a modification and an application of a pre-existing data mining research process known as Knowledge Discovery in Databases (KDD). The KDD process is interactive and generative and involves a series of sequential steps and decision-making processes. The modified KDD process is described to show how it supports secondary data analysis and provides an example of how the modified KDD process was applied across a secondary analysis in mathematics education. This paper provides educational researchers with a practical and iterative framework through which to undertake secondary analysis that enhances flexibility and encourages depth and saturation.

Application of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Element

In a data mining process, outlier detection aims to use the high marginality of these elements to identify them by measuring their degree of deviation from representative patterns, thereby yielding relevant knowledge. Whereas rough sets (RS) theory has been applied to the field of knowledge discovery in databases (KDD) since its formulation in the 1980s; in recent years, outlier detection has been increasingly regarded as a KDD process with its own usefulness. The application of RS theory as a basis to characterise and detect outliers is a novel approach with great theoretical relevance and practical applicability. However, algorithms whose spatial and temporal complexity allows their application to realistic scenarios involving vast amounts of data and requiring very fast responses are difficult to develop. This study presents a theoretical framework based on a generalisation of RS theory, termed the variable precision rough sets model (VPRS), which allows the establishment of a stochastic approach to solving the problem of assessing whether a given element is an outlier within a specific universe of data. An algorithm derived from quasi-linearisation is developed based on this theoretical framework, thus enabling its application to large volumes of data. The experiments conducted demonstrate the feasibility of the proposed algorithm, whose usefulness is contextualised by comparison to different algorithms analysed in the literature.