Knowledge Discovery In Databases Research Articles

Application of Knowledge Discovery in Databases (KDD) to environmental, economic, and social indicators used in BIM workflow to support sustainable design

Life Cycle Sustainability Assessment (LCSA) can help to predict the impact of products and services, such as buildings, during their entire life cycle. However, it is an extensive data method. The Building Information Modelling (BIM) method can contribute towards reducing the effort involved and simplifying the data collection relating to the building elements. To this end, databases adjusted to the BIM workflow are needed to systematise and harmonise the structure of the environmental, economic and social data of said elements. This paper provides a solution to this problem by presenting an innovative Triple Bottom Line (TBL) database with environmental, economic, and social indicators of building elements to support the triple assessment adapted to the BIM workflow. An analysis employing Knowledge Discovery in Databases (KDD) was performed for the first time on this type of database to better understand the correlations between the dimensions. The key contributions include correlation detection, 83 % of which were direct, which showed that, overall, the environmental (CO2 emissions), economic (cost), and social (labour) dimensions experience similar growth trends. Strong correlations between economic and social variables were found in 68 % of the cases, followed by those of the economic and environmental (32 %), and social and environmental (18 %) variables. Findings from the correlation analysis between the three dimensions reveal their influence on the type of building system, element and material. Four scenarios were thereby identified in accordance with these correlations, to aid in sustainable decision-making. Various growth trends were detected, which can facilitate the implementation of the LCSA.

Menu Sales Prediction at Kiyo Café Using Machine Learning

This research evaluates the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in predicting raw material stock for Café Kiyo. The study encompasses six key stages, including preparation, literature review, data collection, data mining processing, results and discussion, and conclusion with recommendations. The data mining process adheres to the Knowledge Discovery in Databases (KDD) framework, involving data selection, preprocessing, transformation, data mining, and interpretation and evaluation. The evaluation metrics reveal that KNN boasts a marginally higher accuracy of 98.71% compared to Naïve Bayes with 98.21%. KNN also demonstrates superior precision (81.25%) in identifying true positives, outperforming Naïve Bayes (72.59%). However, Naïve Bayes excels in recall, achieving 95.15% compared to KNN's 50.00%. The Area Under the Curve (AUC) analysis further confirms Naïve Bayes' superiority, with an AUC value of 0.995, indicating better performance in distinguishing between positive and negative classes.

Distributed Approach for Efficiently Extracting Common Patterns from Large Datasets

Abstract: In recent years, advancements in database knowledge discovery have enabled it to find important and practical information with great force. Frequent pattern mining and association rule mining has been thoroughly investigated for a great range of real-world applications. Traditional mining algorithms use memory-resident, centralised data. The performance is not effective enough when applying these methods to remote databases because of the large amount of data, bandwidth limitation, and energy limitations, especially in this era of big data. Datamining in dispersed contexts has therefore become a significant field of study.

Report on the 3rd International Workshop onLearning to Quantify (LQ 2023)

The 3rd International Workshop on Learning to Quantify (LQ 2023)1 took place on September 18, 2023 in Torino, IT, where it was organised as a satellite event of the 34th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023). Like the main program of the conference, the workshop employed a hybrid format, with all presentations given in presence and with attendees participating in presence or online. This report presents a summary of the workshop, briefly summarising the individual works presented, and touching on the main issues that emerged during the final, open discussion.

Application of Machine Learning and Data Mining in Manufacturing Industry

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

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.

Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics

Social networks and official statistics have become vital sources of information in times of health emergencies. The ability to monitor and profile social sentiment is essential for understanding public perception and response in the context of public health crises, such as the one resulting from the COVID-19 pandemic. This study will explore how social sentiment monitoring and profiling can be conducted using information from social networks and official statistics, and how this combination of data can offer a more complete picture of social dynamics in times of emergency, providing a valuable tool for understanding public perception and guiding a public health response. To this end, a three-layer architecture based on Big Data and Artificial Intelligence is presented: the first layer focuses mainly on collecting, storing, and governing the necessary data such as social media and official statistics; in the second layer, the representation models and machine learning necessary for knowledge generation are built, and in the third layer the previously generated knowledge is adapted for better understanding by crisis managers through visualization techniques among others. Based on this architecture, a KDD (Knowledge Discovery in Databases) framework is implemented using methodological tools such as sentiment analysis, fuzzy 2-tuple linguistic models and time series prediction with the Prophet model. As a practical demonstration of the proposed model, we use tweets as data source (from the social network X, formerly known as Twitter) generated during the COVID-19 pandemic lockdown period in Spain, which are processed to identify the overall sentiment using sentiment analysis techniques and fuzzy linguistic variables, and combined with official statistical indicators for prediction, visualizing the results through dashboards.

PENERAPAN METODE ASOSIASI PADA ANALISA POLA PEMINJAMAN BUKU PERPUSTAKAAN

The library produces a lot of book loan transaction data every day, but the data has not been maximally utilized due to the limited knowledge of the data, therefore the librarian cannot provide the right book recommendations for readers. The research aims to analyze book loan data by applying the Knowledge Discovery in Database (KDD) method. The research stages are observation and interviews, data selection and data preprocessing, data transformation. Data processing using the apriori algorithm association rule mining approach to provide an overview in seeing the pattern of book loan transactions. This is to provide book recommendations that match the reading interests of library members, so that it can become a reference in the layout of books on the shelf according to the results of the rules formed. The book loan transaction data used is the September period of 2023, the implementation uses the rapidminer application to find association rules. The results obtained as many as 77 rule recommendations with the highest support value of 10.7%, the highest confidence value of 100% and the highest lift value of 14. The rule formed is that if a library member borrows a book by Dale Carneige, the chances that the library member will also borrow a book by George Orwel are 100%. The results obtained can be a reference for the library to provide book recommendations to readers, maintain the availability of book stock and arrange the placement of these books on adjacent shelves.

Análisis de Vulnerabilidades de Ciberseguridad Mediante Técnicas de Ciencia de Datos

Cybersecurity analysis using data science and machine learning plays a crucial role in today's digital era, where information security has become imperative for organizations. This study focuses on the intersection between cybersecurity and data science, using machine learning methods and text analytics to understand and strengthen defenses against threats. The importance of TF-IDF (Term-Inverse Document Frequency) is highlighted as a tool to evaluate the relevance of terms in documents and its application in vulnerability classification.The Multinomial Naive Bayes classifier is presented as an efficient tool in text classification, calculating probabilities of belonging to specific classes based on the frequency of terms. The essential formulas used in this algorithm, such as conditional probability and multinomial distribution, are detailed.The KDD (Knowledge Discovery in Databases) methodology guides the process, from datacollection on platforms like Kaggle to data selection, cleaning and transformation. The use of `TfidfVectorizer` facilitates the discretization of text data, and the `GridSearchCV` method optimizes the model's hyperparameters, achieving an accuracy of 97.36%. Finally, the confusion matrix reveals good overall performance, although areas for improvement are identified, especially in the 'High' class.

Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5)

Graduating on time is what every student wants to accomplish in college. Students of Prof. Dr. Hamka Muhammadiyah University are one of those who have this dream. Based on 2020 graduates data from the Tracer Study, 60% said the university had a high enough impact  on improving competence.  This data indicates that university needs to evaluate improvement of academic quality. Often, students have difficulty finding information about important factors that support achieving timely graduation. A prediction analysis is needed to provide information about the student's graduation study period. For this analysis, data mining is implemented using the classification function of the decision tree (C4.5) algorithm with RapidMiner tools. The methodology for implementing data mining follows the stages of Knowledge Discovery In Database (KDD), beginning with data collection, preprocessing, transformation, data mining, and evaluation. The research findings consist of visualization and decision tree rules that reveal GPA as the most influential factor in determining a student's study period.There is other information, namely, students graduated on time (less than equal to 4 years) amounted to 170 or 54.5% and students did not graduate on time (more than 4 years) amounted to 142 or 45.6%. Testing the performance of decision tree (C4.5) utilizing confusion matrix through RapidMiner tools, resulted in accuracy reaching 83.87%, with precision of 87.50% and recall of 91.18%. Provides evidence that the decision tree algorithm (C4.5) has optimal performance to provide valuable information about predicting student graduation in order to increase student enrollment with the right study period.

Penerapan Metode K-Means Clustering Dalam Pengembangan Strategi Promosi Berbasis Data Penerimaan Mahasiswa Baru (Studi Kasus :Universitas Nurul Huda)

The admission of new students is a key element in the success of a university. In its effort to enhance efficiency in admitting new students, Nurul Huda University proposes applying the K-Means Clustering method as a solution to manage candidate student data more effectively and intelligently. The data used in this research is derived from the Admission of New Students process for the academic years 2021/2022 and 2022/2023, totaling 1275 entries, which will then be processed using data mining techniques to generate analysis. The goal is to determine promotional strategies based on the origin of profiles of newly enrolled students. The method applied is clustering with the K-Means algorithm. After data processing, analysis is conducted using the Knowledge Discovery in Databases (KDD) technique, consisting of five stages: selection, preprocessing, transformation, data mining, and evaluation. The implementation in this research utilizes Rapidminer software, resulting in three data clusters: Cluster 1 with 345 entries, covering 27% of the total; Cluster 2 with 86 entries (7%); and Cluster 3 with 835 entries (66%). For promotion, the marketing team is deployed to districts dominant in the East OKU region and potential areas outside the East OKU District. They conduct direct visits to introduce Nurul University to students, distribute brochures, display pamphlets, and adapt strategies using a promotion mix strategy

Penentuan Asosiation Rule Pada Penjualan Produk UMKM Tugu Mulyo Menggunakan Metode Apriori

Pondok Roti has many existing variants, ranging from chocolate bread, mocca bread, round bread, donut bread, coconut bread, strawberry bread and pineapple bread, green bean bread, birthday cake, burgers, hot dogs, pizza, so the bread that is produced must be right so that the bread can be sold out without any stale and moldy bread because it is not sold. The number of unsold breads will harm the business owner. Transactions that occur in a day are quite a lot in this bread business. Sales transactions are still recorded manually using excel, and existing data has not been managed properly to become new information that can help the management in bread production. The many types and flavors of bread make it easy for buyers to choose and buy the bread they want and like. Looking at existing transaction data, it can be seen that buyers prefer certain flavors. Knowledge Discovery in Databases (KDD) is used to explain how the process of extracting information hidden in the database. Knowledge Discovery in Databases (KDD) and data mining are related to each other. This research uses the apriori algorithm to get a rule base for purchasing products at Pondok Roti stores. The apriori algorithm will later be used to find the most frequent combination of an itemset. Research data will be simulated to get the best rule base using the Weka application. The results of the research are in the form of association rules on the sale of Tugu Mulyo MSME products.

J48-Based Decision Tree Algorithm in Detecting Kolb’s Learning Style Preferences of Information Technology Students

Teacher education institutions developed interventions to augment both the Teacher’s teaching and the Learner’s learning process. Previous studies focused on detecting learning styles in e-learning using learning management systems and adaptive learning in the online learning process, specifically using the Felder-Silverman learning style model. On the contrary, this study aimed to develop a decision tree-based model to detect the Learner’s learning style inspired by Kolb’s learning style in a face-to-face learning environment. Knowledge discovery in databases through data mining was utilized using the J48 algorithm to develop a decision tree-based model. This study was participated by 408 out of 462 information technology students in a state university in the Philippines. The study’s result was able to develop four J48-based decision trees with conditional rule models for activist, reflector, theorist and pragmatist learners. The evaluation of the decision tree models using confusion matrix and receiver operating curve showed a very high accuracy detection of every learning style (weighted average of 88-96%). This result recommends applying this in an actual system or computer application for easy and fast learning style detection based on the characteristics of the learners.

Intelligent algorithms applied to the prediction of air freight transportation delays

PurposeThe objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.Design/methodology/approachThe research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).FindingsShipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.Originality/valueThese findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

Analysis of Car Dealership Sales Using Business Intelligence Technology

The growing volume of sales increases the importance of analysis tools, most of which are built on the principles of knowledge discovery in databases, which raises the problem of choosing optimal schemes for storing and processing OLAP cubes. The article presents the structure of the auto dealer's analytical system. A data warehouse structure and a set of models for the sales analysis process of a dealership auto center have been developed. The methodology of car dealer sales analysis is established. The article also notes that in modern economic conditions, it is especially important to monitor trends in decline or growth in the activity of auto centers, to identify promising and unpromising types of components, as well as those products that require certain efforts to manufacture and promote them on the market. Carrying out intelligent data analysis allows you to formulate specific and targeted management decisions in terms of increasing the synergistic effect from product sales, which is especially important in modern economic conditions. The article describes the methodology for analyzing car dealer sales. A data warehouse structure was developed, statistical data and data sets for analysis were collected, and a set of models and reports was created to analyze dealership sales. The effectiveness of the proposed research is demonstrated. Keywords: sales, predictive analysis, sales, data mining, data segmentation.

An Enhanced Multiple Classifier System For Predicting Students’ Academic Outcome

Data-driven decision support approaches have been increasingly employed in recent years to unveil purposeful task-oriented patterns from accumulated students’ academic records. Educational data mining and Knowledge Discovery in Database (KDD) provide a viable solution to decipher implicit knowledge through predictive modelling. Most approaches used the induction of the individual classification models also known as the single classifiers and few efforts focuses on the ensemble methods; which are unstable, overfits and susceptible to skewed data or a poorly performing classifier. To overcome this work examines the existing multiple classification algorithms, Pandey and Taruna approach and proposes an enhanced MCS that uses bagging strategy to reduce variance and perhaps improves accuracy of the resulting model to improve the accuracy. The approach is based on the definition of a model that integrates several classification techniques to predict academic performance of undergraduate students. Data were collected, cleaned, preprocessed and integrated using MS (excel and access) and Weka software. Experiments were carried out using WEKA and EMCS improved using python library. The result shows that the proposed (EMCS) has an increase of 1.03% and 0.63% of accuracy over the Bagging MCS and Pandy& Taruna respectively

APPLICATIONS OF KNOWLEDGE DISCOVERY AND DATABASE (KDD) IN CLINICAL DECISION SUPPORT SYSTEM

Applications of Knowledge Discovery and Database (KDD) in Clinical Decision Support Systems) is a research work that aims to develop a medical system that has the ability to detect and suggest cure for an ailment with minimal effort. Healthcare system has been a major source of worry across the globe in recent time due to emergence of different types of diseases and epidemics. The number of health care personnel in various hospitals falls short of the number required most especially in terms of specialists. The medical system in Nigeria today suffers from lack of fast, accurate, reliable and intelligent software solutions that can help healthcare practitioners make decisions that would solve urgent, and in some cases, complex medical problems in real-time. Also, the cost of processing and analyzing large volumes of data in a medical environment is high most especially in terms of time consumption. So in this research design and implement clinical decision support system using knowledge discovery in database (KDD) was the major focus. The design required a computerized database for storing medical records as well as software for improved intelligence-based medical procedures that were going to be simpler to use, versatile, adaptable, savvy, and effortless at combining and evaluating medical data, allowing medical practitioners at all levels to make realistic, intelligent, and real-time decisions on critical health issues. The system was designed using the Object-Oriented Design and Analysis Methodology (OOADM).

Penerapan Data Mining dengan Metode K-Nearest Neighbor untuk Memprediksi Penjualan Aksesoris Aquarium

PT. Surya Jaya Aquarium is an industry engaged in the sale of various types of aquarium accessories, such as aquarium machines, filter media, aquarium lights. Currently, PT. Surya Jaya Aquarium often lacks certain items when ordering goods from customers. Meanwhile, there is often an excess of other goods at the same time due to a lack of orders from customers. For this reason, it is necessary to carry out the process of predicting product sales at the company, so that the process of controlling product orders can be carried out. To carry out the prediction process, the K-Nearest Neighbor algorithm can be applied. The purpose of this algorithm is to classify new objects using features and training data samples. The data used in this study is sales data for aquarium accessories products sourced from sales for the last 3 years from 2020, 2021 and 2022 originating from PT. Surya Jaya Aquarium. After that the data is selected and will be used to be processed in predicting sales of aquarium accessories for the next period. The K-Nearest Neighbor technique is used in this study to model data that has been prepared using the Knowledge Discovery in Databases (KDD) stage. From the results of the tests carried out, information was obtained that the error rate (error) from the sales prediction results was 6,196%

INCREMENTAL LEARNING FOR EDGE NETWORK INTRUSION DETECTION

The paper presents incremental learning as a solution for adapting intrusion detection systems to the dynamic edge network conditions. Extreme gradient boost trees are proposed and evaluated with the Network Security Laboratory - Knowledge Discovery in Databases (NSL-KDD) benchmark dataset. The accuracy of the XGBoost classifier model improves by 15% with 1% of the KDD-test+ data used for training. A mechanism based on unsupervised learning that triggers retraining of the XGBoost classifier is suggested. These results are relevant in the context of model retraining on resource scarce environments (relative to a cloud environment), such as the network edge or edge devices.

Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review.

Knowledge discovery in databases (KDD) is crucial in analyzing data to extract valuable insights. In medical outcome prediction, KDD is increasingly applied, particularly in diseases with high incidence, mortality, and costs, like cancer. ML techniques can develop more accurate predictive models for cancer patients' clinical outcomes, aiding informed healthcare decision-making. However, cancer prediction modeling faces challenges because of the unbalanced nature of the datasets, where there is a small minority category of patients with a cancer diagnosis compared to a majority category of cancer-free patients. Imbalanced datasets pose statistical hurdles like bias and overfitting when developing accurate prediction models. This systematic review focuses on breast cancer prediction articles published from 2008 to 2023. The objective is to examine ML methods used in three critical steps of KDD: preprocessing, data mining, and interpretation which address the imbalanced data problem in breast cancer prediction. This work synthesizes prior research in ML methods for breast cancer prediction. The findings help identify effective preprocessing strategies, including balancing and feature selection methods, robust predictive models, and evaluation metrics of those models. The study aims to inform healthcare providers and researchers about effective techniques for accurate breast cancer prediction.