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Journal articles on the topic 'Knowledge Discovery in Databases (KDD)'

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Published: 4 June 2021
Last updated: 8 June 2024

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1

Piatetsky-Shapiro, Gregory. "Knowledge discovery in databases: Progress report." Knowledge Engineering Review 9, no. 1 (March 1994): 57–60. http://dx.doi.org/10.1017/s0269888900006573.

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As the number and size of very large databases continues to grow rapidly, so does the need to make sense of them. This need is addressed by the field called knowledge Discovery in Databases (KDD), which combines approaches from machine learning, statistics, intelligent databases, and knowledge acquisition. KDD encompasses a number of different discovery methods, such as clustering, data summarization, learning classification rules, finding dependency networks, analysing changes, and detecting anomalies (Matheus et at., 1993).
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Chen, Po-Chi, Ru-Fang Hsueh, and Shu-Yuen Hwang. "An ILP Based Knowledge Discovery System." International Journal on Artificial Intelligence Tools 06, no. 01 (March 1997): 63–95. http://dx.doi.org/10.1142/s0218213097000050.

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Interest in research into knowledge discovery in databases (KDD) has been growing continuously because of the rapid increase in the amount of information embedded in real-world data. Several systems have been proposed for studying the KDD process. One main task in a KDD system is to learn important and user-interesting knowledge from a set of collected data. Most proposed systems use simple machine learning methods to learn the pattern. This may result in efficient performance but the discovery quality is less useful. In this paper, we propose a method to integrated a new and complicated machine learning method called inductive logic programming (ILP) to improve the KDD quality. Such integration shows how this new learning technique can be easily applied to a KDD system and how it can improve the representation of the discovery. In our system, we use user's queries to indicate the importance and interestingness of the target knowledge. The system has been implemented on a SUN workstation using the Sybase database system. Detailed examples are also provided to illustrate the benefit of integrating the ILP technique with the KDD system.
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Köster, Frank, and Marco Grawunder. "Eine Anwendung von Knowledge Discovery in Databases im eLearning (An Application of Knowledge Discovery in Databases in eLearning)." i-com 2, no. 2/2003 (February 1, 2003): 21–28. http://dx.doi.org/10.1524/icom.2.2.21.19590.

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ZusammenfassungDieser Artikel behandelt die Entwicklung eines Assistenzsystems für Nutzer elektronischer Lehr-/Lernmaterialien (eLLM). Dabei wird das simulatorbasierte Pilotentraining als konkrete Beispielanwendung betrachtet. In diesem Kontext wird insbesondere die mögliche Isolation von Nutzern eLLM als Problem hervorgehoben. Arbeiten zu Tutoriellen Systemen, Virtuellen Lerngemeinschaften, Lernarrangements o.Ä. diskutieren ein facettenreiches Instrumentarium zur Behandlung dieses Problems und prägen eben-so unseren Ansatz. Dieser zielt darauf ab, eine tutorielle Unterstützung sowie Aufgaben zur Bildung/Festigung virtueller Lerngemeinschaften an spezielle Softwarekomponenten (Agenten) zu delegieren, die den Nutzern uneingeschränkt als Assistenten zur Verfügung stehen. Bei der Implementierung der Verhaltensgrundlage dieser Agenten verfolgen wir einen datengetriebenen Zugang, wobei Methoden des Knowledge Discovery in Databases (KDD) zur Anwendung kommen. Die in diesem Zusammenhang erzielten Ergebnisse stellen den Schwerpunkt dieses Artikels dar. Sie umfassen Werkzeuge zum KDD sowie Resultate ihrer Anwendung.
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4

Mishra, Divya, and Ravindra Kumar. "Knowledge Discovery in Databases (KDD): A Comparative Evaluation of Scientific Databases." Asian Journal of Information Science and Technology 7, no. 2 (November 5, 2017): 28–30. http://dx.doi.org/10.51983/ajist-2017.7.2.154.

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In this information explosion age, a large number of commercial and free online database provided by publishers of information resources is available on web, Libraries of every kind offering various services regarding use of online resources and services to fulfill the information requirements of a large group of users. The present study comparatively analyze the selected databases which aims to serves a scientific community. The Library Science and information personnel all over the world are focusing more and more on development of better, user friendly and affordable discovery solutions to fulfill the requirements of patrons.
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5

Hao, Wu. "On Business-Oriented Knowledge Discovery and Data Mining." Advanced Materials Research 760-762 (September 2013): 2267–71. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.2267.

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This paper will discuss issues in data mining and business processes including Marketing, Finance and Health. In turn, the use of KDD in the complex real-world databases in business and government will push the IT researchers to identify and solve cutting-edge problems in KDD modelling, techniques and processes. From IT perspectives, some issues in economic sciences consist of business modelling and mining, aberrant behavior detection, and health economics. Some issues in KDD include data mining for complex data structures and complex modelling. These novel strategies will be integrated to build a one-stop KDD system.
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Głowania, Szymon, Jan Kozak, and Przemysław Juszczuk. "Knowledge Discovery in Databases for a Football Match Result." Electronics 12, no. 12 (June 17, 2023): 2712. http://dx.doi.org/10.3390/electronics12122712.

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The analysis of sports data and the possibility of using machine learning in the prediction of sports results is an increasingly popular topic of research and application. The main problem, apart from choosing the right algorithm, is to obtain data that allow for effective prediction. The article presents a comprehensive KDD (Knowledge Discovery in Databases) approach that allows for the appropriate preparation of data for sports prediction on sports data. The first part of the article covers the subject of KDD and sports data. The next section presents an approach to developing a dataset on top football leagues. The developed datasets are the main purpose of the article and have been made publicly available to the research community. In the latter part of the article, an experiment with the results based on heterogeneous groups of classifiers and the developed datasets is presented.
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Jahani, Alireza, Peyman Akhavan, Mostafa Jafari, and Mohammad Fathian. "Conceptual model for knowledge discovery process in databases based on multi-agent system." VINE Journal of Information and Knowledge Management Systems 46, no. 2 (May 9, 2016): 207–31. http://dx.doi.org/10.1108/vjikms-01-2015-0003.

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Purpose Knowledge discovery in databases (KDD) is a tedious and repetitive process. A challenge for the effective use of KDD is understanding and confirming its results derived from the harmonized process. To exploit the advantages of agents’ application, this paper aims to propose a conceptual model based on a multi-agent system (MAS) to control each step of the KDD process. Design/methodology/approach This paper reports the empirical findings of a survey conducted among academic and industrial sectors in Tehran, Iran. In this survey, the participants answered a questionnaire about the main factors of designing a suitable model for the KDD process based on MAS. The factor analysis reveals important insights of previous models developed by various researchers. Findings This research uses the survey results to find six critical success factors, continuity in refinement and improvement; learning and acting concurrently; loosely or tightly coupled approach for using technologies; cooperative, dynamic and flexible environment; documentation and reporting; and extracting and evaluating knowledge intelligently, for a proper conceptual model of the KDD process based on MAS. Research limitations/implications The proposed model reflects all aspects of the KDD process by applying the intelligent agents for each process steps. In addition, this research only considers the Iran society; hence, it cannot be generalized to other nations, and it may need further research in other countries and to be implemented in real-world business domains. Originality/value This research helps organizations to adopt a proposed model and implement a KDD process to advantage the valuable knowledge that exists in their data resources.
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Storti, Edoardo, Laura Cattaneo, Adalberto Polenghi, and Luca Fumagalli. "Customized Knowledge Discovery in Databases methodology for the Control of Assembly Systems." Machines 6, no. 4 (October 2, 2018): 45. http://dx.doi.org/10.3390/machines6040045.

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The advent of Industry 4.0 has brought to extremely powerful data collection possibilities. Despite this, the potential contained in databases is often partially exploited, especially focusing on the manufacturing field. There are several root causes of this paradox, but the crucial one is the absence of a well-established and standardized Industrial Big Data Analytics procedure, in particular for the application within the assembly systems. This work aims to develop a customized Knowledge Discovery in Databases (KDD) procedure for its application within the assembly department of Bosch VHIT S.p.A., active in the automotive industry. The work is focused on the data mining phase of the KDD process, where ARIMA method is used. Various applications to different lines of the assembly systems show the effectiveness of the customized KDD for the exploitation of production databases for the company, and for the spread of such a methodology to other companies too.
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Qin, Yuan Bo, and Dong Xin Lu. "The Application of KDD in HIS." Applied Mechanics and Materials 263-266 (December 2012): 1510–14. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1510.

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This paper summarizes the concepts of AI(artificial intelligence), ML(machine learning) and KDD (knowledge discovery in databases), including the development, definition, 5 knowledge types, 7 tasks, processes and technologies of KDD. It also introduces the HIS(hospital information system), including the introduction and benefits and HIS in China. At last the paper illustrates the application of KDD in HIS, especially in detecting and explaining key information from databases.
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Jantan, Hamidah, Abdul Razak Hamdan, and Zulaiha Ali Othman. "Managing talent in human resource : a Knowledge Discovery in Database (KDD) approach." Social and Management Research Journal 6, no. 1 (June 1, 2009): 51. http://dx.doi.org/10.24191/smrj.v6i1.5169.

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In any organization, managing human talent is very important and need more attentions from Human Resource (HR) professionals. Nowadays, among the challenges of HR professionals is to manage an organization’s talent, especially to ensure the right person is assigned to the right job at the right time. Knowledge Discovery in Database (KDD) is a data analysis approach that is commonly used for classification and prediction; and this approach has been widely used in many fields such as manufacturing, development, finance and etc. However, this approach has not attracted people in human resource especially for talent management. For this reason, this paper presents an overview of some talent management problems that can be solved by using KDD approach. In this study, we attempt to implement one of the talent management tasks i.e. identifying potential talent by predicting their performance. The employee’s performance can be predicted based on the past experience knowledge which is discovered from existing databases. Finally, this paper proposes the suggested framework for talent management using KDD approach.
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11

Važan, Pavel, Pavol Tanuska, Dominika Jurovatá, and Michal Kebisek. "Analysis of Production Process Parameters by Using Data Mining Methods." Applied Mechanics and Materials 309 (February 2013): 342–49. http://dx.doi.org/10.4028/www.scientific.net/amm.309.342.

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This article deals with knowledge discovery in databases (abbr. KDD) and methodology of this process. The authors give an identification of production parameters and their influence on a production process. Knowledge discovery in the production databases is minimally used for the process of planning and control. There are many problems that occur in the production process. It is important to indentify the impact of manufacturing parameters on the system for managers. New discovered knowledge from production systems will help make the right decision to fulfill the objectives. Using the KDD in the control of production systems, it can be achieved better understanding of system control and can help predict a future behavior of system. The authors formulated general knowledge for improve parameters of analyzed production process. The objectives, steps and some results of the project are presented in this article
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Kasemsap, Kijpokin. "Knowledge Discovery and Data Visualization." International Journal of Organizational and Collective Intelligence 7, no. 3 (July 2017): 56–69. http://dx.doi.org/10.4018/ijoci.2017070105.

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This article reviews the literature in the search for the theories and perspectives of knowledge discovery and data visualization. The literature review highlights the overview of knowledge discovery; Knowledge Discovery in Databases (KDD); Knowledge Discovery in Textual Databases (KDT); the overview of data visualization; the significant perspectives on data visualization; data visualization and big data; and data visualization and statistical literacy. Knowledge discovery is the process of searching for hidden knowledge in the massive amounts of data that individuals are technically capable of generating and storing. Data visualization is an easy way to convey concepts in a universal manner. Organizations, that utilize knowledge discovery and data visualization, are more likely to find both knowledge and information they need when they need them. The findings present valuable insights and further understanding of the way in which knowledge discovery and data visualization efforts should be focused.
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Zemmouri, EL Moukhtar, Hicham Behja, Abdelaziz Marzak, and Brigitte Trousse. "Ontology-Based Knowledge Model for Multi-View KDD Process." International Journal of Mobile Computing and Multimedia Communications 4, no. 3 (July 2012): 21–33. http://dx.doi.org/10.4018/jmcmc.2012070102.

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Knowledge Discovery in Databases (KDD) is a highly complex, iterative and interactive process that involves several types of knowledge and expertise. In this paper the authors propose to support users of a multi-view analysis (a KDD process held by several experts who analyze the same data with different viewpoints). Their objective is to enhance both the reusability of the process and coordination between users. To do so, they propose a formalization of viewpoint in KDD and a Knowledge Model that structures domain knowledge involved in a multi-view analysis. The authors’ formalization, using OWL ontologies, of viewpoint notion is based on CRISP-DM standard through the identification of a set of generic criteria that characterize a viewpoint in KDD.
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Nguyen, Hoan, Tien-Dao Luu, Olivier Poch, and Julie D. Thompson. "Knowledge Discovery in Variant Databases Using Inductive Logic Programming." Bioinformatics and Biology Insights 7 (January 2013): BBI.S11184. http://dx.doi.org/10.4137/bbi.s11184.

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Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research, especially for the development of diagnostics and effective therapeutic solutions. In this work, we describe the use of a recent knowledge discovery from database (KDD) approach using inductive logic programming (ILP) to automatically extract knowledge about human monogenic diseases. We extracted background knowledge from MSV3d, a database of all human missense variants mapped to 3D protein structure. In this study, we identified 8,117 mutations in 805 proteins with known three-dimensional structures that were known to be involved in human monogenic disease. Our results help to improve our understanding of the relationships between structural, functional or evolutionary features and deleterious mutations. Our inferred rules can also be applied to predict the impact of any single amino acid replacement on the function of a protein. The interpretable rules are available at http://decrypthon.igbmc.fr/kd4v/ .
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Guo, Yu Dong. "Prototype System of Knowledge Management Based on Data Mining." Applied Mechanics and Materials 411-414 (September 2013): 251–54. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.251.

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Knowledge is a very crucial resource to promote economic development and society progress which includes facts, information, descriptions, or skills acquired through experience or education. With knowledge has being increasingly prominent, knowledge management has become important measure for the core competences promotion of a corporation. The paper begins with knowledge managements definition, and studies the process of knowledge discovery from databases (KDD),data mining techniques and SECI(Socialization, Externalization, Combination, Internalization) model of knowledge dimensions. Finally, a simple knowledge management prototype system was proposed which based on the KDD and data mining.
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Mariscal, Gonzalo, Óscar Marbán, and Covadonga Fernández. "A survey of data mining and knowledge discovery process models and methodologies." Knowledge Engineering Review 25, no. 2 (June 2010): 137–66. http://dx.doi.org/10.1017/s0269888910000032.

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AbstractUp to now, many data mining and knowledge discovery methodologies and process models have been developed, with varying degrees of success. In this paper, we describe the most used (in industrial and academic projects) and cited (in scientific literature) data mining and knowledge discovery methodologies and process models, providing an overview of its evolution along data mining and knowledge discovery history and setting down the state of the art in this topic. For every approach, we have provided a brief description of the proposed knowledge discovery in databases (KDD) process, discussing about special features, outstanding advantages and disadvantages of every approach. Apart from that, a global comparative of all presented data mining approaches is provided, focusing on the different steps and tasks in which every approach interprets the whole KDD process. As a result of the comparison, we propose a new data mining and knowledge discovery process namedrefined data mining processfor developing any kind of data mining and knowledge discovery project. The refined data mining process is built on specific steps taken from analyzed approaches.
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Pavanelli, Genival, Maria Teresinha Arns Steiner, Anderson Roges Teixeira Góes, Alessandra Memari Pavanelli, and Deise Maria Bertholdi Costa. "Extraction of Classification Rules in Databases through Metaheuristic Procedures Based on GRASP." Advanced Materials Research 945-949 (June 2014): 3369–75. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.3369.

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The process of knowledge management in the several areas of society requires constant attention to the multiplicity of decisions to be made about the activities in organizations that constitute them. To make these decisions one should be cautious in relying only on personal knowledge acquired through professional experience, since the whole process based on this method would be slow, expensive and highly subjective. To assist in this management, it is necessary to use mathematical tools that fulfill the purpose of extracting knowledge from database. This article proposes the application of Greedy Randomized Adaptive Search Procedure (GRASP) as Data Mining (DM) tool within the process called Knowledge Discovery in Databases (KDD) for the task of extracting classification rules in databases.
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Yousefi Naghani, Samira, Rozita Dara, Zvonimir Poljak, and Shayan Sharif. "A review of knowledge discovery process in control and mitigation of avian influenza." Animal Health Research Reviews 20, no. 1 (June 2019): 61–71. http://dx.doi.org/10.1017/s1466252319000033.

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AbstractIn 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.
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Sembiring, Muhammad Ardiansyah, Raja Andri Tama Agus, and Mustika Fitri Larasati Sibuea. "ANALISIS KEPUASAN PELANGGAN MENGGUNAKAN METODE ROUGH SET." JOURNAL OF SCIENCE AND SOCIAL RESEARCH 4, no. 2 (June 29, 2021): 227. http://dx.doi.org/10.54314/jssr.v4i2.647.

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Data mining adalah proses dari Knowledge Discovery from Databases (KDD). KDD adalah kegiatan yang meliputi pengumpulan, pemakaian data, historis untuk menemukan keteraturan, pola atau hubungan dalam set data besar. Metode Rough Set berhubungan dengan discreet data, rough set biasanya digunakan bersamaan dengan teknik lain untuk melakukan discreetization pada dataset. Tujuan utama dari analisis rough set adalah untuk mensintesis pendekatan konsep dari data yang diperoleh. Penelitian ini bertujuan untuk mengetahui tingkat kepuasan pelanggan
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Kumar, Pardeep, Vivek Kumar Sehgal, and Durg Singh Chauhan. "Knowledge Discovery in Databases KDD with Images: A Novel Approach toward Image Mining and Processing." International Journal of Computer Applications 27, no. 6 (August 31, 2011): 10–13. http://dx.doi.org/10.5120/3307-4531.

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Rahman, Fauziah Abdul, Muhammad Ishak Desa, Antoni Wibowo, and Norhaidah A. Haris. "An Improvement of Knowledge Discovery Database (KDD) Framework for Effective Decision." Journal of Artificial Intelligence 9, no. 4 (September 15, 2016): 72–77. http://dx.doi.org/10.3923/jai.2016.72.77.

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Muhammad Suleiman, Muhammad, Mukhtar Ibrahim Bello, Shilpa ., and Muhammed Kuliya. "Application of Data Mining Techniques in Education: A Review." Journal of Applied Science, Information and Computing 1, no. 1 (June 30, 2020): 62–71. http://dx.doi.org/10.59568/jasic-2020-1-1-08.

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Data mining is the process that analyzes large data to find fresh and unknown information that increases industry productivity. In the field of trying, to discover novel and potentially suitable data, DM also be called KDD (Knowledge Discovery in Databases). Currently, DM has been introduced with educational settings, which is called EDM, it is an area of systematic analysis focused on the improvement of discovery approaches in the exclusive types of facts from academic situations and using this approach to effectively understood learners and the settings wherein they learn. EDM is emerging that focuses on analyzing educational data to develop models for improving experiences and efficiency in teaching and learning. Growing popularity in DM and the system of education is transforming educational data mining into a modern, rising research culture. Educational data mining involves removing hidden knowledge from vast educational datasets utilizing techniques and resources such as sorting, decision tree, clustering algorithms, etc. to create novel methods of knowledge exploration from educational databases, which is used for educational assessment which decision-making.
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Obeidat, Ibrahim, Nabhan Hamadneh, Mouhammd Alkasassbeh, Mohammad Almseidin, and Mazen Ibrahim AlZubi. "Intensive Pre-Processing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 01 (January 29, 2019): 70. http://dx.doi.org/10.3991/ijim.v13i01.9679.

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Abstract— Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanism that used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity and availability of the services. The speed of the IDS is very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The techniques J48, Random Forest, Random Tree, MLP, Naïve Bayes and Bayes Network classifiers have been chosen for this study. It has been proven that the Random forest classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type (DOS, R2L, U2R, and PROBE).
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YANG, BINGRU, JIANGTAO SHEN, and WEI SONG. "KDK BASED DOUBLE-BASIS FUSION MECHANISM AND ITS PROCESS MODEL." International Journal on Artificial Intelligence Tools 14, no. 03 (June 2005): 399–423. http://dx.doi.org/10.1142/s021821300500217x.

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Knowledge Discovery in Knowledge Base (KDK) opens new horizons for research. KDK and KDD (Knowledge Discovery in Database) are the different cognitive field and discovery process. In most people's view, they are independent each other. In this paper we can summarize the following tasks: Firstly, we discussed that two kinds of the process model and mining algorithm of KDK based on facts and rules in knowledge base. Secondly, we proves that the inherent relation between KDD and KDK (i.e. double-basis fusion mechanism). Thirdly, we gained the new process model and implementation technology of KDK*. Finally, the imitation experimentation proved that the validity of above mechanism and process model.
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Li, Jing Min, Jin Yao, and Yong Mou Liu. "A Model for Acquisition of Implicit Design Knowledge Based on KDD." Materials Science Forum 505-507 (January 2006): 505–10. http://dx.doi.org/10.4028/www.scientific.net/msf.505-507.505.

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Knowledge discovery in database (KDD) represents a new direction of data processing and knowledge innovation. Design is a knowledge-intensive process driven by various design objectives. Implicit knowledge acquisition is key and difficult for the intelligent design system applied to mechanical product design. In this study, the characteristic of implicit design knowledge and KDD are analyzed, a model for product design knowledge acquisition is set up, and the key techniques including the expression and application of domain knowledge and the methods of knowledge discovery are discussed. It is illustrated by an example that the method proposed can be used to obtain the engineering knowledge in design case effectively, and can promote the quality and intelligent standard of product design.
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GLAVAN, ALINA, and VICTOR CROITORU. "INCREMENTAL LEARNING FOR EDGE NETWORK INTRUSION DETECTION." REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 68, no. 3 (October 12, 2023): 301–6. http://dx.doi.org/10.59277/rrst-ee.2023.3.9.

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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.
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Sofyan, Fazrin Meila Azzahra, Affani Putri Riyandoro, Devi Fitriani Maulana, and Jajam Haerul Jaman. "Penerapan Data Mining dengan Algoritma C5.0 Untuk Prediksi Penyakit Stroke." J-SISKO TECH (Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD) 6, no. 2 (July 21, 2023): 619. http://dx.doi.org/10.53513/jsk.v6i2.8578.

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Penyakit stroke merupakan kondisi yang mempengaruhi sistem saraf dan dapat menyebabkan dampak yang serius pada kesehatan seseorang. WHO menyatakan sebanyak 13,7 juta kasus setiap tahunnya dan 5,5 juta orang diantaranya meninggal dunia akibat penyakit ini. Tujuan dari penelitian ini adalah untuk mengembangkan model prediksi yang dapat membantu dalam identifikasi dini risiko terjadinya stroke. Metode yang digunakan dalam penelitian ini adalah Knowledge Discovery in Databases (KDD) dengan menerapkan algoritma C5.0, yang merupakan salah satu algoritma klasifikasi yang efektif dalam mengolah data dengan atribut numerik maupun kategorikal. Pada metode Knowledge Discovery in Databases (KDD) terdiri dari beberapa tahap yang perlu dilakukan untuk penelitian ini, yaitu selection, preprocessing, transformation, data mining, dan evaluation. Untuk Algoritma C5.0 sendiri merupakan sebuah algoritma klasifikasi dalam bidang data mining yang secara khusus digunakan dalam teknik decision tree. Data yang digunakan dalam penelitian ini adalah dataset yang berisi informasi medis dan faktor risiko yang terkait dengan stroke. Hasil dari penelitian ini berupa Decision Tree (pohon keputusan) dengan nilai accuracy, recall, dan precision dengan melakukan split data 80% (data training) - 20% (data testing) hasil nilai Accuracy yang diperoleh sebesar 95%, Recall = 96%, dan Precision = 99%.
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Schroeder, Lucas, Mauricio Roberto Veronez, Eniuce Menezes de Souza, Diego Brum, Luiz Gonzaga, and Vinicius Francisco Rofatto. "Respiratory Diseases, Malaria and Leishmaniasis: Temporal and Spatial Association with Fire Occurrences from Knowledge Discovery and Data Mining." International Journal of Environmental Research and Public Health 17, no. 10 (May 25, 2020): 3718. http://dx.doi.org/10.3390/ijerph17103718.

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The relationship between the fires occurrences and diseases is an essential issue for making public health policy and environment protecting strategy. Thanks to the Internet, today, we have a huge amount of health data and fire occurrence reports at our disposal. The challenge, therefore, is how to deal with 4 Vs (volume, variety, velocity and veracity) associated with these data. To overcome this problem, in this paper, we propose a method that combines techniques based on Data Mining and Knowledge Discovery from Databases (KDD) to discover spatial and temporal association between diseases and the fire occurrences. Here, the case study was addressed to Malaria, Leishmaniasis and respiratory diseases in Brazil. Instead of losing a lot of time verifying the consistency of the database, the proposed method uses Decision Tree, a machine learning-based supervised classification, to perform a fast management and extract only relevant and strategic information, with the knowledge of how reliable the database is. Namely, States, Biomes and period of the year (months) with the highest rate of fires could be identified with great success rates and in few seconds. Then, the K-means, an unsupervised learning algorithms that solves the well-known clustering problem, is employed to identify the groups of cities where the fire occurrences is more expressive. Finally, the steps associated with KDD is perfomed to extract useful information from mined data. In that case, Spearman’s rank correlation coefficient, a nonparametric measure of rank correlation, is computed to infer the statistical dependence between fire occurrences and those diseases. Moreover, maps are also generated to represent the distribution of the mined data. From the results, it was possible to identify that each region showed a susceptible behaviour to some disease as well as some degree of correlation with fire outbreak, mainly in the drought period.
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Jannah, Eka Roehatul, and Martanto. "KLATERISASI DATA PENDUDUK BERDASARKAN PEKERJAAN MENGGUNAKAN METODE K-MEANS PADA WILAYAH JAWA BARAT." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 2 (April 3, 2024): 1709–16. http://dx.doi.org/10.36040/jati.v8i2.9055.

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Perkembangan teknologi merupakan peluang yang tepat memperoleh data dengan lebih efektif dan efisien. Data mining adalah salah satu komponen dalam proses Knowledge Discovery in Databases (KDD). KDD adalah suatu rangkaian proses yang bertujuan menemukan informasi yang bermanfaat dari sumber data dalam database. Permasalahan dalam penelitian ini, bagaimana jika Metode K-Means mungkin tidak sesuai untuk mengelompokkan data penduduk berdasarkan pekerjaan?. Penelitian ini bertujuan untuk mengidentifikasi pola pekerjaan penduduk wilayah Jawa Barat dan membentuk kelompok pekerjaan yang serupa. Melalui metode K-Means, akan memungkinkan saya untuk mengelompokkan penduduk Jawa Barat berdasarkan jenis pekerjaan mereka menggunakan tahapan KDD. Dengan tahapan KDD kita dapat dengan mudah melihat data penduduk berdasarkan pekerjaan dari tahun 2011-2023. Dapat diambil kesimpulan bahwa penduduk yang bekerja dengan nilai tertinggi adalah pada Cluster 3 yang ditandai dengan warna biru (tinggi) berjumlah 151 items, untuk data pekerjaan dengan nilai sedang berada pada Cluster 2 yang ditandai dengan warna oranye (sedang) berjumlah 100 items, dan ntuk penjualan dengan nilai terendah yaitu pada Cluster 0 dan Cluster 1 yang ditandai dengan warna hijau dan hitam (rendah) dengan jumlah yang sama yaitu 50 items. Hasil percobaan yang dilakukan pada data penduduk berdasarkan pekerjaan menggunakan metode DBI (Davies Bouldin Index), menghasilkan nilai K terbaik pada cluster 4 yaitu 0,262.
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Wulandari, Cindi, Lukman Sunardi, and Pebrian Syaifudin. "Penentuan Asosiation Rule Pada Penjualan Produk UMKM Tugu Mulyo Menggunakan Metode Apriori." Bulletin of Computer Science Research 4, no. 1 (December 31, 2023): 18–26. http://dx.doi.org/10.47065/bulletincsr.v4i1.303.

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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.
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Archanjo, Gabriel A., and Fernando J. Von Zuben. "Genetic Programming for Automating the Development of Data Management Algorithms in Information Technology Systems." Advances in Software Engineering 2012 (July 5, 2012): 1–14. http://dx.doi.org/10.1155/2012/893701.

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Information technology (IT) systems are present in almost all fields of human activity, with emphasis on processing, storage, and handling of datasets. Automated methods to provide access to data stored in databases have been proposed mainly for tasks related to knowledge discovery and data mining (KDD). However, for this purpose, the database is used only to query data in order to find relevant patterns associated with the records. Processes modelled on IT systems should manipulate the records to modify the state of the system. Linear genetic programming for databases (LGPDB) is a tool proposed here for automatic generation of programs that can query, delete, insert, and update records on databases. The obtained results indicate that the LGPDB approach is able to generate programs for effectively modelling processes of IT systems, opening the possibility of automating relevant stages of data manipulation, and thus allowing human programmers to focus on more complex tasks.
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Cavalcante, Fabricio, Lianderson Ribeiro, and Zoroastro Vilar. "Integração com SCADA para gestão de indicadores de manutenção em parques eólicos." Revista Eletrônica de Engenharia Elétrica e Engenharia Mecânica 3, no. 2 (December 6, 2021): 15–23. http://dx.doi.org/10.21708/issn27635325.v3n2.a10532.2021.

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Atualmente, a gestão da manutenção possui um papel estratégico dentro da indústria. A crescente demanda pela alta disponibilidade e maior confiabilidade dos ativos, abre espaço para aplicação de técnicas e metodologias de Engenharia de Manutenção, visando identificar os principais empecilhos para cumprimento destes objetivos, baseados em Confiabilidade, Manutenabilidade e Disponibilidade. Este artigo apresentará a aplicação do método de Knowledge Discovery in Databases – KDD, associado ao diagrama de Jack-Knife, para elencar os padrões de falhas numa usina eólica.
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Quintella, Rogério Hermida, and Jair Sampaio Soares Junior. "Sistemas de apoio à decisão e descoberta de conhecimento em bases de dados: uma aplicação potencial em políticas públicas." Organizações & Sociedade 10, no. 28 (December 2003): 83–98. http://dx.doi.org/10.1590/s1984-92302003000400006.

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Entre os movimentos recentes da área de TI na esfera pública está o desenvolvimento de sistemas que permitem análises e suportam a tomada de decisão a partir de problemas pouco estruturados. Entre estes sistemas, destacam-se aqueles usualmente conhecidos como Sistemas de Apoio à Decisão - SAD e Knowledge Discovery in Databases - KDD. O objetivo deste trabalho é o de estruturar a análise desses conceitos e sua aplicação na área pública, mais especificamente, discutir uma iniciativa em fase de implantação no Estado da Bahia.
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Mirza, Ahmad Haidar. "Poverty Data Model as Decision Tools in Planning Policy Development." Scientific Journal of Informatics 5, no. 1 (May 21, 2018): 39. http://dx.doi.org/10.15294/sji.v5i1.14022.

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Poverty is the main problem in a country both in developing countries to the developed countries, both in structural poverty, cultural and natural. That is, poverty is no longer seen as a measure of the failure of the Government to protect and fulfill the fundamental rights of its citizens but as a challenge of the nation to realize a fair society, prosperous and dignified sovereign. Various efforts have been made in determining government policy measures in an effort to overcome poverty, one of them by conducting a survey to assess the poor. The results of the survey of the various activities of the organization obtained a variety of database versions poverty to areas or locations. The information generated from the poverty database only includes recapitulation of poor people to the area or location. One step is to process the data on poverty in a process of Knowledge Discovery in Databases (KDD) to form a data mining poverty. Data mining is a logical combination of knowledge of data, and statistical analysis developed in the knowledge business or a process that uses statistical techniques, mathematics, artificial intelligence, artificial and machine-learning to extract and identify useful information for the relevant knowledge from various large databases.
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Bouaita, Bilal, Abdesselem Beghriche, Akram Kout, and Abdelouahab Moussaoui. "A New Approach for Optimizing the Extraction of Association Rules." Engineering, Technology & Applied Science Research 13, no. 2 (April 2, 2023): 10496–500. http://dx.doi.org/10.48084/etasr.5722.

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Association rule methods are among the most used approaches for Knowledge Discovery in Databases (KDD), as they allow discovering and extracting hidden meaningful relationships between attributes or items in large datasets in the form of rules. Algorithms to extract these rules require considerable time and large memory spaces. This paper presents an algorithm that decomposes this complex problem into subproblems and processes items by category according to their support. Very frequent items and fairly frequent items are studied together. To evaluate the performance of the proposed algorithm, it was compared with Eclat and LCMFreq on two actual transactional databases. The experimental results showed that the proposed algorithm was faster in execution time and demonstrated its efficiency in memory consumption.
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Siregar, Vanessa, and Paska Marto Hasugian. "Application of Data Mining Method Using Association Rules Apriori To Shopping Cart Analysis On Sale Transactions (Case Study Alfamidi Burnt Stone)." Journal Of Computer Networks, Architecture and High Performance Computing 2, no. 2 (June 1, 2020): 222–26. http://dx.doi.org/10.47709/cnapc.v2i2.425.

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Also Often data mining is called knowledge discovery in databases (KDD), ie activities include the collection, historical use of data to find regularities, patterns or relationships in data sets with a large size. The company may be interested to know if some groups consistently goods items purchased together. This study analyzes the transaction of data information retrieval from the sale of skin care and hair care using data mining algorithms priori Alfamidi Burnt Stones with the highest support value is 8% and the highest value is 5% confidance
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V. Rafael, Felicisima. "Predicting Job Change among Data Scientists using Machine Learning Technique." 14th GCBSS Proceeding 2022 14, no. 2 (December 28, 2022): 1. http://dx.doi.org/10.35609/gcbssproceeding.2022.2(77).

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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
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Rodríguez-Ruiz, Julieta G., Carlos Eric Galván-Tejada, Sodel Vázquez-Reyes, Jorge Issac Galván-Tejada, and Hamurabi Gamboa-Rosales. "Classification of Depressive Episodes Using Nighttime Data: Multivariate and Univariate Analysis." Proceedings of the Institute for System Programming of the RAS 33, no. 2 (2021): 115–24. http://dx.doi.org/10.15514/ispras-2021-33(2)-6.

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Mental disorders like depression represent 28% of global disability, it affects around 7.5% percent of global disability. Depression is a common disorder that affects the state of mind, normal activities, emotions, and produces sleep disorders. It is estimated that approximately 50% of depressive patients suffering from sleep disturbances. In this paper, a data mining process to classify depressive and not depressive episodes during nighttime is carried out based on a formal method of data mining called Knowledge Discovery in Databases (KDD). KDD guides the process of data mining with stages well established: Pre-KDD, Selection, Pre-processing, Transformation, Data Mining, Evaluation, and Post-KDD. The dataset used for the classification is the DEPRESJON dataset, which contains the motor activity of 23 unipolar and bipolar depressed patients and 32 healthy controls. The classification is carried out with two different approaches; a multivariate and univariate analysis to classify depressive and non-depressive episodes. For the multivariate analysis, the Random Forest algorithm is implemented with a model construct of 8 features, the results of the classification are specificity equal to 0.9927 and sensitivity equal to 0.9991. The univariate analysis shows that the maximum of the activity is the most descriptive characteristic of the model with 0.908 in accuracy for the classification of depressive episodes.
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R Vora, Deepali, and Kamatchi Iyer. "EDM – survey of performance factors and algorithms applied." International Journal of Engineering & Technology 7, no. 2.6 (March 11, 2018): 93. http://dx.doi.org/10.14419/ijet.v7i2.6.10074.

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Educational Data Mining (EDM) is a new field of research in the data mining and Knowledge Discovery in Databases (KDD) field. It mainly focuses in mining useful patterns and discovering useful knowledge from the educational information systems from schools, to colleges and universities. Analysing students’ data and information to perform various tasks like classification of students, or to create decision trees or association rules, so as to make better decisions or to enhance student’s performance is an interesting field of research. The paper presents a survey of various tasks performed in EDM and algorithms (methods) used for the same. The paper identifies the lacuna and challenges in Algorithms applied, Performance Factors considered and data used in EDM.
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Zunaidi, Muhammad, Vina Winda Sari, and Leni Marsaulina. "Implementasi Data Mining Untuk Menyusun Strategi Promosi Dalam Menetapkan Paket Menu Menggunakan Algoritma Apriori." Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) 22, no. 2 (August 12, 2023): 422. http://dx.doi.org/10.53513/jis.v22i2.8242.

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AbstrakAyam Presto Cabe Hijo Cabang Medan merupakan perusahaan yang bergerak dibidang kuliner di Kota Medan. Namun kondisi perusahaan saat ini menurut evaluasi bulanan memiliki hasil pendapatan yang kurang maksimal, karena proses penjualan menu restoran yang ditawarkan kepada pelanggan kurang maksimal. Menu yang dijual kepada pelanggan di restoran tidak terjual secara merata. Menu yang tidak terjual habis mengakibatkan stock menu di restoran mengalami penumpukan bahan baku makanan, sehingga mengakibatkan kerusakan bahan makanan yang telah lama di stock. Sehingga di butuhkan solusi berupa pengelompokan data transaksi pelanggan yang dilakukan penambangan informasi. Cara ini dikenal dengan istilah data mining. Data Mining adalah suatu istilah yang digunakan untuk menguraikan penemuan pengetahuan didalam database atau sering disebut Knowledge Discovery in Database (KDD).Dari hasil penambangan tersebut, maka akan akan dibuat promosi paket menu dimana akan dilakukan penggabungan makanan dan minuman yang diminati oleh pelanggan dan yang kurang diminati, sehingga penjualan makanan dan minuman akan menjadi seimbang dan bisa meningkatkan penjualan. Dengan menggunakan algoritma apriori dapat mengetahui berapa banyak yang terjual makanan dan minuman yang muncul bersamaan dalam suatu transaksi.AbstractChicken Presto Cabe Hijo Medan Branch is a company engaged in the culinary field in the city of Medan. However, based on the monthly evaluation, the company's current condition is that revenue is not optimal, because the restaurant menu sales process offered to customers is not optimal. Menus sold to customers in restaurants are not sold evenly. Menus that are not sold out cause menu stock in restaurants to accumulate food raw materials, resulting in damage to food ingredients that have been in stock for a long time. So a solution is needed in the form of grouping customer transaction data carried out by information mining. This method is known as data mining. Data Mining is a term used to describe the discovery of knowledge in databases or often called Knowledge Discovery in Database (KDD). From the mining results a menu package promotion will be made that will combine food and beverages that are of interest to customers and those that are less desirable, so that food and beverage sales are balanced and can increase sales. By using the a priori algorithm, you can find out how much food and drink that is sold appears together in a transaction.
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41

Srinivas A Vaddadi, Abhilash Maroju, Sravanthi Dontu, Rohith Vallabhaneni,. "An Intrusion Detection System (Ids) Schemes for Cybersecurity in Software Defined Networks." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (August 31, 2023): 837–43. http://dx.doi.org/10.17762/ijritcc.v11i9s.9491.

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The process of analysing and improving network traffic is of tremendous relevance to network management and multimedia data mining techniques. Security in Software Defined Networks (SDNs), which rely on a programmable controller in the middle, has recently emerged as the most challenging aspect of SDNs. Network traffic monitoring is critical for detecting and exposing intrusion anomalies in an SDN context. Thus, this study offers a thorough assessment of the NSL-KDD dataset using five separate clustering algorithms: K-means, Farthest First, Canopy, Density-based method, and Exception-maximization (EM). The software used to conduct the comparisons is the Waikato Environment for Knowledge Analysis (WEKA). In addition, the article introduces a knowledge discovery in databases (KDD)–based deep learning (DL) model for intrusion detection that is SDN-based. Initially, the dataset that is being used is clustered into four main attack types and one normal category. We will next go over the steps necessary to build a deep learning intrusion detection system that is based on SDN. The results provide an objective assessment of the several attack types present in the KDD dataset. Just like other methods, the results demonstrate that the proposed deep learning strategy provides better intrusion detection performance. As an illustration, the tested dataset demonstrates a detection accuracy of 94.21% when using the suggested approach.
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Olanrewaju, Oyenike Mary, Faith Oluwatosin Echobu, and Abubakar Mogaji. "MODELLING OF AN INTRUSION DETECTION SYSTEM USING C4.5 MACHINE LEARNING ALGORITHM." FUDMA JOURNAL OF SCIENCES 4, no. 4 (June 14, 2021): 454–59. http://dx.doi.org/10.33003/fjs-2020-0404-502.

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The increasing growth of wireless networking and new mobile computing devices has caused boundaries between trusted and malicious users to be blurred. The shift in security priorities from the network perimeter to information protection and user resources security is an open area for research which is concerned with the protection of user information’s confidentiality, integrity and availability. Intrusion detection systems are programs or software applications embedded in sophisticated devices to monitor the activities on networks or systems for security, policy or protocol violation or malicious activities detection. In this work, an intrusion detection model was proposed using C4.5 algorithm which was implemented with WEKA tool and RAPID MINER. The model showed good performance when trained and tested with validation techniques. Implementation of the proposed model was conducted on the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) dataset, an improved version of KDD 99 dataset, which showed that the proposed model approach has an average detection rate of 99.62% and reduced false alarm rate of 0.38%.
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43

Gallagher, James, and Christopher M. Smith. "Market basket applications on short web links." International Journal of Market Research 62, no. 2 (January 1, 2019): 139–57. http://dx.doi.org/10.1177/1470785318818408.

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Market research is an indispensable part of an organization’s ability to understand market dynamics. Over the past 20 years, data collection and analysis through Knowledge Discovery through Databases (KDD) has arisen to supplement the traditional methods of surveys and focus groups. Market Basket Analysis is a discipline of KDD that identifies associations between commonly purchased items. As social media use has grown, link shortening companies help users share links in a constrained space environment and, in exchange, collect data about each user when a link is clicked. This research applies market basket analysis techniques with graph mining to shortened web link data to identify communities of co-visited websites to help analysts better understand web traffic for a geographic area during a time range. Patterns within clusters of web domains regarding hardware platforms, operating systems, or referral sources are then identified and used to gain a better understanding of a geographic area.
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Andrade-Arenas, Laberiano, Inoc Rubio-Paucar, and Cesar Yactayo-Arias. "Data mining for predictive analysis in gynecology: a focus on cervical health." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (June 1, 2024): 2822. http://dx.doi.org/10.11591/ijece.v14i3.pp2822-2833.

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Currently, data mining based on the application of detection of important patterns that allow making decisions according to cervical cancer is a problem that affects women from the age of 24 years and older. For this purpose, the Rapid Miner Studio tool was used for data analysis according to age. To perform this analysis, the knowledge discovery in databases (KDD) methodology was used according to the stages that this methodology follows, such as data selection, data preparation, data mining and evaluation and interpretation. On the other hand, the comparison of methodologies such as the standard intersectoral process for data mining (Crips-dm), KDD and sample, explore, modify, model, evaluate (Semma) is shown, which is separated by dimensions and in each dimension both methodologies are compared. In that sense, a graph was created comparing algorithmic models such as naive Bayes, decision tree, and rule induction. It is concluded that the most outstanding result was -1.424 located in cluster 4 in the attribute result date.
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Tripathi, Aditya Kumar, and Anu Sharma. "Techniques for Data Mining Prediction in the Health Care Sector." International Journal of Innovative Research in Computer Science and Technology 11, no. 3 (May 3, 2023): 32–37. http://dx.doi.org/10.55524/ijircst.2023.11.3.6.

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Data mining is another term for knowledge discovery in databases (KDD). It's an interdisciplinary field that focuses on rooting meaningful knowledge from data in all sectors similar as health, education, and business. Currently, with the covid epidemic affecting everyone and rising coronavirus cases causing nursing home beds, oxygen, vaccines and individuals to be denied by hospitals, the health structure of the elderly is in the spotlight. There's a wealth of information accessible in the medical world about these diseases. Data booby-trapping concepts may be used to prize meaningful styles from this type of material in order to prognosticate unborn followings. This study emphasizes on several mining approaches that will be applied in the therapy assiduity to achieve the stylish results.
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Molina Huerta, Carlos, Alan Sotelo Atahua, Jahir Villacrisis Guerrero, and Laberiano Andrade-Arenas. "Data mining: Application of digital marketing in education." Advances in Mobile Learning Educational Research 3, no. 1 (January 30, 2023): 621–29. http://dx.doi.org/10.25082/amler.2023.01.011.

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The excessive cost of inadequate management of stored information resources by companies means a significant loss for them, causing them to invest more than they should in technology. To overcome and avoid more significant losses, companies must counteract this type of problem. The present work's aim is to apply good data mining through digital business marketing that will allow ordering and filtering of the relevant information in the databases through RapidMiner, to supply the companies' databases with only relevant information for the normal development of their functions. For this purpose, the Knowledge Discovery Databases (KDD) methodology will be used, which will allow us to filter and search for information patterns that are hidden in order to take advantage of the historical data of investment per student in the educational sector and to establish a more accurate and efficient data prediction. As a result, it was found that over the years, the expenditure per student increases regardless of the area in which it is located, that although not in all provinces same amount is allocated, it is observed that it maintains an upward trend concerning the expenditures made, concluding that the KDD methodology allowed us to graph and showed how the expenditure allocated to the education sector has varied in the different grades of education, providing relevant information that will be useful for future related studies.
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Shaaban, Amani Gomaa, Mohamed Helmy Khafagy, Mohamed Abbas Elmasry, Heba El-Beih, and Mohamed Hasan Ibrahim. "Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (June 1, 2022): 1736. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1736-1746.

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Recently due <span>to the explosion in the data field, there is a great interest in the data science areas such as big data, artificial intelligence, data mining, and machine learning. Knowledge gives control and power in numerous manufacturing areas. Companies, factories, and all organizations owners aim to benefit from their huge; recorded data that increases and expands very quickly to improve their business and improve the quality of their products. In this research paper, the knowledge discovery in databases (KDD) technique has been followed, “association rules” algorithms “Apriori algorithm”, and “chi-square automatic interaction detection (CHAID) analysis tree” have been applied on real datasets belonging to (Emisal factory). This factory annually loses tons of production due to the breakdowns that occur daily inside the factory, which leads to a loss of profit. After analyzing and understanding the factory product processes, we found some breakdowns occur a lot of days during the product lifecycle, these breakdowns affect badly on the production lifecycle which led to a decrease in sales. So, we have mined the data and used the mentioned methods above to build a predictive model that will predict the breakdown types and help the factory owner to manage the breakdowns risks by taking accurate actions before the breakdowns happen.</span>
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Džeroski, Sašo, and Jan Struyf. "5th international workshop on knowledge discovery in inductive databases (KDID'06)." ACM SIGKDD Explorations Newsletter 9, no. 1 (June 2007): 56–58. http://dx.doi.org/10.1145/1294301.1294303.

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Syahpitri Damanik, Nur Afni, Irianto Irianto, and Dahriansah Dahriansah. "Penerapan Metode Clustering Dengan Algoritma K-Means Tindak Kejahatan Pencurian di Kabupaten Asahan." J-Com (Journal of Computer) 1, no. 1 (February 25, 2021): 7–14. http://dx.doi.org/10.33330/j-com.v1i1.1065.

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Abstract:Theft is the illegal taking of property or belongings of another person without the permission of the owner. The most common crime problem in Asahan District is theft, so that the POLRES is still having trouble determining which areas are often the crime of theft. With this problem, we need to do a grouping for areas where theft often occurs, so the process used is the data mining process. Data mining is one of the processes of Knowledge Discovery from Databases (KDD). KDD is an activity that includes collecting, using historical data to find regularities, patterns or relationships in large data sets. One of the techniques known in data mining is clustering technique. The K-Means method is a method for clustering techniques, K- Means is a method that partitions data into groups so that data with the same characteristics are entered into the same set of groups and data with different characteristics are grouped into other groups. The attributes used in grouping this data are annual data, namely 2015, 2016, 2017, 2018, 2019. A case study of 9 POLSEK in the Asahan. Keywords: Data Mining, Clustering, K-Means Algorithm, Theft Crimes Grouping. Abstrak: Pencurian merupakan pengambilan properti atau barang milik orang lain secara tidak sah tanpa ijin dari pemilik. Masalah tindak kejahatan yang paling banyak terjadi di Kabupaten Asahan adalah tindak kejahatan pencurian sehingga pihak POLRES masih kesulitan untuk menentukan daerah mana saja yang sering terjadi tindak kejahatan pencuriaan. Dengan adanya masalah ini kita perlu melakukan pengelompokan untuk daerah mana saja yang sering terjadi tindak pencurian maka proses yang digunakan adalah proses data mining. Data mining adalah salah satu proses dari Knowledge Discovery from Databases (KDD). KDD adalah kegiatan yang meliputi pengumpulan, pemakaian data, historis untuk menemukan keteraturan, pola atau hubungan dalam set data besar. Salah satu teknik yang di kenal dalam data mining adalah teknik clustering. Metode K-Means merupakan metode untuk teknik clustering, K-Means adalah metode yang mempartisi data kedalam kelompok sehingga data berkarakteristik sama dimasukan kedalam set kelompok yang sama dan data yang berkerakteristik berbeda dikelompokkan ke dalam kelompok yang lain. Atribut yang di gunakan dalam pengelomokan data ini adalah data pertahun yaitu tahun 2015, 2016, 2017, 2018, 2019. Studi kasus pada 9 POLSEK yang ada di daerah kabupaten Asahan. Kata kunci: Data Mining, Clustering, Algoritma K-Means, Pengelompokan Tindak Kejahatan Pencurian.
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50

Peng, Yong Jun, Yang Peng, and Ci Fang Liu. "Research on Database and Information Technology in the Decision Tree of Communication Construction Scheme." Advanced Materials Research 1046 (October 2014): 461–64. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.461.

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With the development of database technology as well as the widespread application of Database management system, our capabilities of both generating and collecting data have been increasing rapidly. In addition, popular use of the World Wide Web as a global information system has flooded us with a tremendous amount of data and information. This explosive growth in stored or transient data has generated an urgent need for new techniques and automated tools that can intelligently assist us in transforming the vast amounts of data into useful information and knowledge. The Data Mining technology brought forward. Data mining, also popularly referred to knowledge discovery from data (KDD), is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large databases, data warehouses, the Web, other massive information repositories, or data streams. Data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization. Database and Information Technology in the Decision Tree of is very important for the military. The Data Mining had been applied and studied in these years, and it has been applied in many domains successfully, such as business, finance and medical treatment. However, little is applied in communication construction scheme.
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