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Journal articles on the topic 'Data mining technologies'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Komoliddinqizi, Quldasheva Nargiza, Begimov Oybek Mamarasulovich, and Zarpullayev Urolbek Xudayaro'g'li. "Modern data mining technologies." ACADEMICIA: An International Multidisciplinary Research Journal 10, no. 4 (2020): 657. http://dx.doi.org/10.5958/2249-7137.2020.00129.9.

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2

Lu, Johan. "Editorial: [XML technologies and Data Mining]." Open Information Systems Journal 3, no. 2 (September 1, 2009): 68. http://dx.doi.org/10.2174/1874133900903020068.

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3

Loginova, Lyudmila N., and Alexander M. Shash. "Data Mining technologies in managing the assortment of trading companies." Journal of Applied Informatics 16, no. 91 (February 26, 2021): 99–109. http://dx.doi.org/10.37791/2687-0649-2021-16-1-99-109.

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In the conditions of fierce competition, satisfaction of all customer needs provides a trading enterprise with a sustainable competitive advantage. With the traditional structure of the assortment, there is a decrease in both the potential and real level of profit, the loss of competitive positions in promising markets, and, therefore, there is a decrease in the stability of the enterprise. The development of an analysis system to determine the specifics of the product range, optimize the range, and adapt it to the conditions of the Russian market is undoubtedly an urgent task. This article provides an overview of trade and IT companies that use data mining technologies. The survey showed that many companies are using data mining technology to improve customer service, turnover and sales in stores. In this regard, the management of Familia decided to develop its own software that will combine the analysis of turnover and sales in the company's stores in order to increase sales and improve the placement of goods in stores so that the client buys the necessary things, increasing the company's profit. The paper shows the possibility of combining several data mining methods in one system; shows the results of the analysis system and shows the effectiveness of the developed analysis system at Familia. The uniqueness of the developed software is the combination of data mining algorithms into one software product. The developed analysis system, based on the joint work of two data mining algorithms K-means and Apriori, allows you to manage the range of trade enterprises, reducing company losses.

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4

-Marcotorchino, Jean-François. "Les technologies avancées de l'analyse de l'information : Text Mining, Data Mining et Fusion Data Mining-Text Mining." Revue de l'Electricité et de l'Electronique -, no. 07 (2001): 56. http://dx.doi.org/10.3845/ree.2001.077.

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5

Wang, Lidong, and Guanghui Wang. "Data Mining Applications in Big Data." Computer Engineering and Applications Journal 4, no. 3 (September 20, 2015): 143–52. http://dx.doi.org/10.18495/comengapp.v4i3.155.

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Data mining is a process of extracting hidden, unknown, but potentially useful information from massive data. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in data mining and technologies in Big Data. Challenges of data mining and data mining with big data are discussed. Some technology progress of data mining and data mining with big data are also presented.

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Lu, Johan. "Hot Topic: [XML technologies and Data Mining]." Open Information Systems Journal 3, no. 1 (September 1, 2009): 68–145. http://dx.doi.org/10.2174/1874133900903010068.

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7

Lemke, Frank, and Johann-Adolf Müller. "Medical data analysis using self-organizing data mining technologies." Systems Analysis Modelling Simulation 43, no. 10 (October 2003): 1399–408. http://dx.doi.org/10.1080/02329290290027337.

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8

Madukwe, Atkinson Ike, Jennifer Somtochukwu Madukwe, Justina Ogochukwu Osonwa, and Obiora Chukwuemerie Ernest. "The Impact of Emerging Technologies on Data Mining." Asian Journal of Science and Applied Technology 10, no. 1 (May 15, 2021): 24–26. http://dx.doi.org/10.51983/ajsat-2021.10.1.2814.

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Business analytics has improved tremendously in recent past providing business leaders’ insights, particularly from operational data stored in transactional system. An example is e-commerce data analysis, which has recently come to be viewed as a killer appropriate for the field of data mining.

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9

Marinov, Miroslav, Abu Saleh Mohammad Mosa, Illhoi Yoo, and Suzanne Austin Boren. "Data-Mining Technologies for Diabetes: A Systematic Review." Journal of Diabetes Science and Technology 5, no. 6 (November 2011): 1549–56. http://dx.doi.org/10.1177/193229681100500631.

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10

Zaslavskaya, Veronika L. "CURRENT TECHNOLOGIES, METHODS AND TECHNIQUES OF DATA MINING." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 11/1, no. 131 (2022): 151–64. http://dx.doi.org/10.36871/ek.up.p.r.2022.11.01.019.

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The relevance of this topic has especially increased in the era of the digital age, since we use various types of information in absolutely every area and sphere of life, but it becomes especially relevant in relation to business. In the XXI century, information is transmitted with incredible speed and, with proper data analysis, can become the most valuable asset of a company. This can help companies improve certain aspects of their products and services, as well as the overall brand image and customer service quality. With the help of data analysis, you can discover the weaknesses and strengths of your competitors, opening up opportunities for increasing the potential of competitiveness.

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11

Wang, Shuliang, and Hanning Yuan. "Spatial Data Mining." International Journal of Data Warehousing and Mining 10, no. 4 (October 2014): 50–70. http://dx.doi.org/10.4018/ijdwm.2014100103.

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Big data brings the opportunities and challenges into spatial data mining. In this paper, spatial big data mining is presented under the characteristics of geomatics and big data. First, spatial big data attracts much attention from the academic community, business industry, and administrative governments, for it is playing a primary role in addressing social, economic, and environmental issues of pressing importance. Second, humanity is submerged by spatial big data, such as much garbage, heavy pollution and its difficulties in utilization. Third, the value in spatial big data is dissected. As one of the fundamental resources, it may help people to recognize the world with population instead of sample, along with the potential effectiveness. Finally, knowledge discovery from spatial big data refers to the basic technologies to realize the value of big data, and relocate data assets. And the uncovered knowledge may be further transformed into data intelligences.

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12

Kaur, Simranjit, and Seema Baghla. "Data Mining Approach in Retail Knowledge Discovery and Internet Technologies." Asian Journal of Engineering and Applied Technology 7, no. 2 (November 5, 2018): 100–105. http://dx.doi.org/10.51983/ajeat-2018.7.2.998.

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Online shopping has a shopping channel or purchasing various items through online medium. Data mining is defined as a process used to extract usable data from a larger set of any raw data. The data set extraction from the demographic profiles and Questionnaire to investigate the gathered based by association. The method for shopping was totally changed with the happening to internet Technology. Association rule mining is one of the important problems of data mining has been used here. The goal of the association rule mining is to detect relationships or associations between specific values of categorical variables in large data sets.

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13

Gul, Sumeer, Shohar Bano, and Taseen Shah. "Exploring data mining: facets and emerging trends." Digital Library Perspectives 37, no. 4 (October 20, 2021): 429–48. http://dx.doi.org/10.1108/dlp-08-2020-0078.

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Purpose Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data. Design/methodology/approach An extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field. Findings The study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences. Practical implications The paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful. Originality/value The paper tries to highlight the current trends and facets of data mining.

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14

Bathla, Gourav, Himanshu Aggarwal, and Rinkle Rani. "Migrating From Data Mining to Big Data Mining." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 13. http://dx.doi.org/10.14419/ijet.v7i3.4.14667.

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Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples. Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.

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15

Meng, Jian Liang, Yan Yan Yang, and Wei Hua Niu. "The Research of Commonly Used Technologies and Application Fields on Data Mining." Applied Mechanics and Materials 130-134 (October 2011): 282–85. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.282.

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Data mining is a computer analysis technology to birth not long but with rapid development, in these years, data mining theories have matured increasingly, and have shined in practice. This paper introduces briefly the concept of data mining, discusses commonly used data mining technologies, then the applications of data mining are introduced in detail, and expresses good perspective to future of data mining finally.

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16

Bellazzi, Riccardo, and Ameen Abu-Hanna. "Data Mining Technologies for Blood Glucose and Diabetes Management." Journal of Diabetes Science and Technology 3, no. 3 (May 2009): 603–12. http://dx.doi.org/10.1177/193229680900300326.

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Data mining is the process of selecting, exploring, and modeling large amounts of data to discover unknown patterns or relationships useful to the data analyst. This article describes applications of data mining for the analysis of blood glucose and diabetes mellitus data. The diabetes management context is particularly well suited to a data mining approach. The availability of electronic health records and monitoring facilities, including telemedicine programs, is leading to accumulating huge data sets that are accessible to physicians, practitioners, and health care decision makers. Moreover, because diabetes is a lifelong disease, even data available for an individual patient may be massive and difficult to interpret. Finally, the capability of interpreting blood glucose readings is important not only in diabetes monitoring but also when monitoring patients in intensive care units. This article describes and illustrates work that has been carried out in our institutions in two areas in which data mining has a significant potential utility to researchers and clinical practitioners: analysis of (i) blood glucose home monitoring data of diabetes mellitus patients and (ii) blood glucose monitoring data from hospitalized intensive care unit patients.

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17

Hebert, Daniel, Billie Anderson, Alan Olinsky, and J. Michael Hardin. "Time Series Data Mining." International Journal of Business Analytics 1, no. 4 (October 2014): 51–68. http://dx.doi.org/10.4018/ijban.2014100104.

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Modern technologies have allowed for the amassment of data at a rate never encountered before. Organizations are now able to routinely collect and process massive volumes of data. A plethora of regularly collected information can be ordered using an appropriate time interval. The data would thus be developed into a time series. Time series data mining methodology identifies commonalities between sets of time-ordered data. Time series data mining detects similar time series using a technique known as dynamic time warping (DTW). This research provides a practical application of time series data mining. A real-world data set was provided to the authors by dunnhumby. A time series data mining analysis is performed using retail grocery store chain data and results are provided.

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18

Mitroshin, Pavel, Yulia Shitova, Yury Shitov, Dmitry Vlasov, and Anton Mitroshin. "Big Data and Data Mining Technologies Application at Road Transport Logistics." Transportation Research Procedia 61 (2022): 462–66. http://dx.doi.org/10.1016/j.trpro.2022.01.075.

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19

Uzar, Ceren. "The Usage of Data Mining Technology in Financial Information System." International Journal of Finance & Banking Studies (2147-4486) 3, no. 1 (January 21, 2014): 51–61. http://dx.doi.org/10.20525/ijfbs.v3i168.

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Data mining technology is one of the new technologies that have become increasingly popular. Data mining enables to form forecasts and models regarding future by making use of past data. It can be costly, risky and time consuming for enterprises to gain knowledge. Firms gain important competitive advantage by data mining methods. This study analyzes on the readiness to implement and the extent of utilization of data mining technologies in the Financial Information Systems (FIS) in Borsa Istanbul and also researches the level of understanding of, perceptions of and readiness to implement data mining technologies within the Borsa Istanbul. Analysis was undertaken using SPSS. Manufacturing and financial enterprises are the universal of this study. Primary data were obtained by using survey method and questionnaire technique and findings of the study were evaulated. Technological, organisational and human resources issues had a significant role in the decision to, or not to utilize data mining technology. The ability to use data mining technology was found to be increased the performance of the Financial Information System.

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20

Bolnokin, V. E., D. I. Mutin, E. I. Mutina, V. G. Vyskub, and O. Ja Kravets. "Data mining technologies in a distributed medical information system." Journal of Physics: Conference Series 2094, no. 3 (November 1, 2021): 032005. http://dx.doi.org/10.1088/1742-6596/2094/3/032005.

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Abstract Proposed a method for solving the problem of identifying hidden relationships in hard-to-structure data that have an implicit character is considered using information mining. Proposed decision trees, the effectiveness of which is illustrated by a specific example. The use of OLAP analysis systems on data presented using in the form of a real or virtual hypercube’s information is an effective tool for the effectiveness of the management for medical monitoring

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21

Li, Yang, Yan Qiang Li, and Zhi Xue Wang. "Fault Diagnosis of Automobile ECUs with Data Mining Technologies." Applied Mechanics and Materials 40-41 (November 2010): 156–61. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.156.

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With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.

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22

Li, Jing Zhu, Hui Ling Wu, and Tai Yu Liu. "Data Mining Architecture System Expansion." Advanced Materials Research 926-930 (May 2014): 1898–901. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1898.

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How to build a data warehouse and data mining is constantly worth exploring and optimization, not only technically, in commercial applications as well. This article discusses the introduction of new technologies and concepts, the traditional method of data warehouse technology has changed dramatically, based on data warehouse applications are a new development. Each enterprise data warehouse based on the characteristics of different companies, you can use a very flexible method of selection and design selection, implementation. According to some relatively new technical features, talk about data warehousing and data mining architecture.

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23

Bognár, Eszter Katalin. "Novel IT Technologies on the Digital Battlefield: The Application of Big Data and Data Mining Technologies." Hadmérnök 15, no. 4 (2020): 141–58. http://dx.doi.org/10.32567/hm.2020.4.10.

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In modern warfare, the most important innovation to date has been the utilisation of information as a weapon. The basis of successful military operations is the ability to correctly assess a situation based on credible collected information. In today’s military, the primary challenge is not the actual collection of data. It has become more important to extract relevant information from that data. This requirement cannot be successfully completed without necessary improvements in tools and techniques to support the acquisition and analysis of data. This study defines Big Data and its concept as applied to military reconnaissance, focusing on the processing of imagery and textual data, bringing to light modern data processing and analytics methods that enable effective processing.

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Oliverio, Jared. "A Survey of Social Media, Big Data, Data Mining, and Analytics." Journal of Industrial Integration and Management 03, no. 03 (September 2018): 1850003. http://dx.doi.org/10.1142/s2424862218500033.

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Big Data is a very popular term today. Everywhere you turn companies and organizations are talking about their Big Data solutions and Analytic applications. The source of the data used in these applications varies. However, one type of data is of great interest to most organizations, Social Media Data. Social Media applications are used by a large percentage of the world’s population. The ability to instantly connect and reach other people and companies over distributed distances is an important part of today’s society. Social Media applications allow users to share comments, opinions, ideas, and media with friends, family, businesses, and organizations. The data contained in these comments, ideas, and media are valuable to many types of organizations. Through Data Mining and Analysis, it is possible to predict specific behavior in users of the applications. Currently, several technologies aid in collecting, analyzing, and displaying this data. These technologies allow users to apply this data to solve different problems, in different organizations, including the finance, medicine, environmental, education, and advertising industries. This paper aims to highlight the current technologies used in Data Mining and Analyzing Social Media data, the industries using this data, as well as the future of this field.

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Ma, Hong Yu, and Gui Yun Zhang. "The Origin and Development of Data Mining with Large Data." Applied Mechanics and Materials 668-669 (October 2014): 1331–34. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1331.

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With rapid information technology development, the accumulation of data and applications are becoming more and more urgent. Data is growing faster and faster, so "massive, explosive growth," words just can't describe today's data growth, data mining from the knowledge or information is found in the repository, which has special meaning for humans to deal with these data provide a convenient and effective way. Between big data and data mining technologies have an inseparable relationship.

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26

Burlakov, Alexandr, and Irina Mushenyk. "Theoretical Principles of Implementation and Use of Modern Technologies of Intellectual Data Analysis in Economy." Modern Economics 25, no. 1 (February 23, 2020): 27–32. http://dx.doi.org/10.31521/modecon.v25(2021)-04.

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Abstract. The aim of the article is to study the theoretical foundations of the introduction and use of modern technologies of data mining in the economy of Ukraine. Research methodology. The theoretical and methodological basis of the article were the works of leading foreign and domestic scientists on the evaluation of the effectiveness of the implementation and use of data mining technologies. Achieving this goal was carried out using the following scientific techniques and research methods: monographic (in reviewing and studying the literature on evaluating the effectiveness of data mining technologies), analysis and synthesis (for research and generalization of research results); logical-theoretical and dialectical (to form the conclusions of the study). In the process of research, the substantiation of theoretical calculations and conclusions was carried out on the basis of system-functional and structural approaches to the analysis of information flows and control systems of data mining technologies. The information base of the study is articles and monographs, including those posted on web pages. Results of the research. The theoretical bases of functioning of modern technologies of data mining in the information economy are systematized, and also questions of the basic directions of application of the Data Mining system are analyzed, namely as a mass product for business applications and as a tool for unique researches. Scientific novelty of research results. It consists in the theoretical substantiation of possibilities of introduction and application of modern technologies of intellectual analysis, in various branches of economy, as effective tools of providing the timely information for the management of business entities on the basis of which it is possible to make qualitative administrative decisions. The practical significance of the research results. The obtained results can be used for further prospective research of information systems of economic entities, which are created on the basis of modern data mining systems, as well as in the implementation of computerization tools for analytical and synthetic data processing of information systems in the enterprise. Keywords: information system; data mining; information technology; data mining.

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Критська, Я. О., T. O. Білобородова, and І. С. Скарга-Бандурова. "Data mining techniques for IoT analytics." ВІСНИК СХІДНОУКРАЇНСЬКОГО НАЦІОНАЛЬНОГО УНІВЕРСИТЕТУ імені Володимира Даля, no. 5(253) (September 5, 2019): 53–62. http://dx.doi.org/10.33216/1998-7927-2019-253-5-53-62.

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Data mining (DM) is one of the most valuable technologies enable to identify unknown patterns and make Internet of Things (IoT) smarter. The current survey focuses on IoT data and knowledge discovery processes for IoT. In this paper, we present a systematic review of various DM models and discuss the DM techniques applicable to different IoT data. Some data specific features were analyzed, and algorithms for knowledge discovery in IoT data were considered.Challenges and opportunities for mining multimodal, heterogeneous, noisy, incomplete, unbalanced and biased data as well as massive datasets in IoT are also discussed.

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MIYATA, Satoshi, and Jun HSU. "F011004 Data Mining and Decision Making Technologies in Simulia Isight." Proceedings of Mechanical Engineering Congress, Japan 2012 (2012): _F011004–1—_F011004–2. http://dx.doi.org/10.1299/jsmemecj.2012._f011004-1.

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Milliaris, A. G., and Mary Milliaris. "Are foreign currency markets interdependent? Evidence from data mining technologies." Estocástica: Finanzas y Riesgo 2, no. 1 (January 30, 2012): 31–47. http://dx.doi.org/10.24275/uam/azc/dcsh/efr/2012v2n1/milliaris.

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Zhou, Qiumei, Robert Dilmore, Andrew Kleit, and John Yilin Wang. "Evaluating gas production performances in marcellus using data mining technologies." Journal of Natural Gas Science and Engineering 20 (September 2014): 109–20. http://dx.doi.org/10.1016/j.jngse.2014.06.014.

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31

Zhou, Qiumei, Robert Dilmore, Andrew Kleit, and John Yilin Wang. "Evaluating Fracture-Fluid Flowback in Marcellus Using Data-Mining Technologies." SPE Production & Operations 31, no. 02 (May 1, 2016): 133–46. http://dx.doi.org/10.2118/173364-pa.

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32

Bāliņa, Signe, Rita Žuka, and Juris Krasts. "Opportunities for the Use of Business Data Analysis Technologies." Economics and Business 28, no. 1 (April 1, 2016): 20–25. http://dx.doi.org/10.1515/eb-2016-0003.

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Abstract The paper analyses the business data analysis technologies, provides their classification and considers relevant terminology. The feasibility of business data analysis technologies handling big data sources is overviewed. The paper shows the results of examination of the online big data source analytics technologies, data mining and predictive modelling technologies and their trends.

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Uzar, Ceren. "The Usage of Data Mining Technology in Financial Information System: An Application on Borsa Istanbul." International Journal of Finance & Banking Studies (2147-4486) 3, no. 1 (January 19, 2016): 51. http://dx.doi.org/10.20525/.v3i1.168.

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<div><p>Data mining technology is one of the new technologies that have become increasingly popular. Data mining enables to form forecasts and models regarding future by making use of past data. It can be costly, risky and time consuming for enterprises to gain knowledge. Firms gain important competitive advantage by data mining methods. This study analyzes on the readiness to implement and the extent of utilization of data mining technologies in the Financial Information Systems (FIS) in Borsa Istanbul and also researches the level of understanding of, perceptions of and readiness to implement data mining technologies within the Borsa Istanbul. Analysis was undertaken using SPSS. Manufacturing and financial enterprises are the universal of this study. Primary data were obtained by using survey method and questionnaire technique and findings of the study were evaulated. Technological, organisational and human resources issues had a significant role in the decision to, or not to utilize data mining technology. The ability to use data mining technology was found to be increased the performance of the Financial Information System.<strong></strong></p></div>

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34

Tzanis, George. "Biological and Medical Big Data Mining." International Journal of Knowledge Discovery in Bioinformatics 4, no. 1 (January 2014): 42–56. http://dx.doi.org/10.4018/ijkdb.2014010104.

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This paper discusses the concept of big data mining in the domain of biology and medicine. Biological and medical data are increasing at very rapid rates, which in many cases outpace even Moore's law. This is the result of recent technological development, as well as the exploratory attitude of human beings, that prompts scientists to answer more questions by conducting more experiments. Representative examples are the advances in sequencing and medical imaging technologies. Challenges posed by this data deluge, and the emerging opportunities of their efficient management and analysis are also part of the discussion. The major emphasis is given to the most common biological and medical data mining applications.

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35

Yang, Gao Ming, Jing Zhao Li, and Shun Xiang Zhang. "Recent Advances in Preserving Privacy Data Mining." Advanced Materials Research 798-799 (September 2013): 541–44. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.541.

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A number of privacy preserving techniques have been proposed recently in data mining. In this paper, we provide a review of the state-of-the-art methods for privacy preserving data mining. and discuss methods for randomization, secure multipart computation, and so on. We also make a classification for the privacy preserving data mining technologies, and analyze some works in this field, such as data distortion method for achieving privacy preserving association rule mining. Detailed evaluation criteria of privacy preserving algorithm were illustrated, which include algorithm performance, data utility, and privacy protection degree. Finally, the development of privacy preserving data mining for further directions is given.

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Comandè, Giovanni, and Giulia Schneider. "Regulatory Challenges of Data Mining Practices: The Case of the Never-ending Lifecycles of ‘Health Data’." European Journal of Health Law 25, no. 3 (April 18, 2018): 284–307. http://dx.doi.org/10.1163/15718093-12520368.

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Abstract Health data are the most special of the ‘special categories’ of data under Art. 9 of the General Data Protection Regulation (GDPR). The same Art. 9 GDPR prohibits, with broad exceptions, the processing of ‘data concerning health’. Our thesis is that, through data mining technologies, health data have progressively undergone a process of distancing from the healthcare sphere as far as the generation, the processing and the uses are concerned. The case study aims thus to test the endurance of the ‘special category’ of health data in the face of data mining technologies and the never-ending lifecycles of health data they feed. At a more general level of analysis, the case of health data shows that data mining techniques challenge core data protection notions, such as the distinction between sensitive and non-sensitive personal data, requiring a shift in terms of systemic perspectives that the GDPR only partly addresses.

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Wu, You, Zheng Wang, and Shengqi Wang. "Human Resource Allocation Based on Fuzzy Data Mining Algorithm." Complexity 2021 (June 10, 2021): 1–11. http://dx.doi.org/10.1155/2021/9489114.

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Data mining is currently a frontier research topic in the field of information and database technology. It is recognized as one of the most promising key technologies. Data mining involves multiple technologies, such as mathematical statistics, fuzzy theory, neural networks, and artificial intelligence, with relatively high technical content. The realization is also difficult. In this article, we have studied the basic concepts, processes, and algorithms of association rule mining technology. Aiming at large-scale database applications, in order to improve the efficiency of data mining, we proposed an incremental association rule mining algorithm based on clustering, that is, using fast clustering. First, the feasibility of realizing performance appraisal data mining is studied; then, the business process needed to realize the information system is analyzed, the business process-related links and the corresponding data input interface are designed, and then the data process to realize the data processing is designed, including data foundation and database model. Aiming at the high efficiency of large-scale database mining, database development tools are used to implement the specific system settings and program design of this algorithm. Incorporated into the human resource management system of colleges and universities, they carried out successful association broadcasting, realized visualization, and finally discovered valuable information.

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Marchuk, G. V., V. L. Levkivskyy, and S. S. Kaliberda. "INTELLECTUAL ANALYSIS OF DATA." Bionics of Intelligence 1, no. 92 (June 2, 2019): 65–70. http://dx.doi.org/10.30837/bi.2019.1(92).11.

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The main research of the article is the data mining methods, such as linear and polynomial regression and the support vector machine. The application success is based on the fact that the methods and technologies of Data mining ensure the study of data and the research of hidden patterns in them. The analysis assists in identification of various features and data parameters, and therefore it is a powerful tool in the stage of forming forecasting models.

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Stahl, Frederic, and Max Bramer. "Scaling up classification rule induction through parallel processing." Knowledge Engineering Review 28, no. 4 (November 26, 2012): 451–78. http://dx.doi.org/10.1017/s0269888912000355.

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AbstractThe fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

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Rahman, Nayem. "Data Mining Problems Classification and Techniques." International Journal of Big Data and Analytics in Healthcare 3, no. 1 (January 2018): 38–57. http://dx.doi.org/10.4018/ijbdah.2018010104.

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Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

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Kuo, Nai Wen. "Healthcare Information System and Data Mining." Applied Mechanics and Materials 55-57 (May 2011): 561–66. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.561.

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This paper is to integrate information technology and medical-related technologies to develop a healthcare information system for comprehensive geriatric assessment. This system not only can process geriatric consultation services and ensure that all patient’s information are stored in standardized format , but also provide medical personnel for statistical analysis and processing purposes. This paper uses the Apriori algorithm of data mining for helping doctors to find out the relationship of geriatric syndrome. The systems of this paper can improve increase the timeliness and accuracy of patient care and administration information, increase service capacity, reduce personnel costs, and improve the quality of patient care in geriatric medicine. Furthermore, making the theories and applications of medical informatics will be more extensive and convenient for researcher and healthcare-related industry.

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Desai, Varsha P., Dr K. S. Oza, and Dr P. G. Naik. "Data Mining Approach for Cyber Security." International Journal of Computer Applications Technology and Research 10, no. 01 (January 6, 2021): 035–41. http://dx.doi.org/10.7753/ijcatr1001.1007.

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Use of internet and communication technologies plays significant role in our day to day life. Data mining capability is leveraged by cybercriminals as well as security experts. Data mining applications can be used to detect future cyber-attacks by analysis, program behavior, browsing habits and so on. Number of internet users are gradually increasing so there is huge challenges of security while working in the cyber world. Malware, Denial of Service, Sniffing, Spoofing, cyber stalking these are the major cyber threats. Data mining techniques are provides intelligent approach for threat detections by monitoring abnormal system activities, behavioral and signatures patterns. This paper highlights data mining applications for threat analysis and detection with special approach for malware and denial of service attack detection with high precision and less time.

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Kousis, Anestis, and Christos Tjortjis. "Data Mining Algorithms for Smart Cities: A Bibliometric Analysis." Algorithms 14, no. 8 (August 17, 2021): 242. http://dx.doi.org/10.3390/a14080242.

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Smart cities connect people and places using innovative technologies such as Data Mining (DM), Machine Learning (ML), big data, and the Internet of Things (IoT). This paper presents a bibliometric analysis to provide a comprehensive overview of studies associated with DM technologies used in smart cities applications. The study aims to identify the main DM techniques used in the context of smart cities and how the research field of DM for smart cities evolves over time. We adopted both qualitative and quantitative methods to explore the topic. We used the Scopus database to find relative articles published in scientific journals. This study covers 197 articles published over the period from 2013 to 2021. For the bibliometric analysis, we used the Biliometrix library, developed in R. Our findings show that there is a wide range of DM technologies used in every layer of a smart city project. Several ML algorithms, supervised or unsupervised, are adopted for operating the instrumentation, middleware, and application layer. The bibliometric analysis shows that DM for smart cities is a fast-growing scientific field. Scientists from all over the world show a great interest in researching and collaborating on this interdisciplinary scientific field.

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Board, Editorial. "4th International conference on Big Data Analysis and Data Mining." Global Journal of Enterprise Information System 8, no. 3 (April 6, 2017): 83. http://dx.doi.org/10.18311/gjeis/2016/15844.

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Conference Series LLC welcomes you to attend the 4th International conference onBig Data Analysis and Data Mining during September 07-08, 2017 in Paris, France.We cordially invite all the participants to share their knowledge and research in the field of Big Data Analysis and Data Mining. Data Mining 2017 anticipates more than 200 participants around the globe with thought provoking Keynote lectures, Oral presentations, Symposiums, Workshops and Poster presentations. The attending delegates include Editorial Board Members of related International Journals. This is an excellent opportunity for the delegates from universities and institutes to interact with the world class scientists and eminent personalities. The intending participants can confirm their participation by registering for the conference along with your colleagues. The main theme of the conference is “Future Technologies for Knowledge Discoveries in Data”.

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Kumar, Prashant, and Khushboo Pandeya. "Big Data and Distributed Data Mining: An Example of Future Networks." International Journal of Advance Research and Innovation 1, no. 2 (2013): 12–15. http://dx.doi.org/10.51976/ijari.121303.

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This paper describes the perspective on the analytics of big data generated by sensors and devices on the edge of networks. The paper includes a discussion of the importance of data at the edge of networks where some of ―biggest‖ big data is generated. Also quick overview of emerging technologies, including distributed frameworks such as the Apache Hadoop framework and Apache* Map Reduce.

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Chouhan, Mayushi, and Rohit Singh Nain. "Mining Big Data: Its Current Status and Future." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 30, 2017): 285. http://dx.doi.org/10.23956/ijarcsse/v7i7/0218.

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Organizations create 2.5 Quintilian bytes of data. So much that 90% of the data in the world today has been set up in the last two years alone. What is Big Data? Big Data is large volumes of structured and unstructured data. This data is what organizations collect on a daily basis. The amount of data is not the important part, but the information gathered from that data is the key. Collecting and analyzing Big Data gives organizations enhanced insight, decision making, and process automation. Approximately each one can agree that big data has taken the business world by storm, but what’s next? Will data continue to grow? What technologies will develop around it? Or will big data become a relic as quickly as the next trend — cognitive technology? Fast data? - appears on the horizon. I believe, am that big data is only going to get bigger and those companies that ignore it will be left further and further behind. This paper studies about what is big data, how does it helps organizations to extract information, its tools and technologies and its future.

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Wang, Yi Ni. "The Study and Application of E-Business Website Based on the Web Data Mining." Advanced Materials Research 846-847 (November 2013): 1431–34. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1431.

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Sun, Wencheng, Zhiping Cai, Yangyang Li, Fang Liu, Shengqun Fang, and Guoyan Wang. "Data Processing and Text Mining Technologies on Electronic Medical Records: A Review." Journal of Healthcare Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/4302425.

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Currently, medical institutes generally use EMR to record patient’s condition, including diagnostic information, procedures performed, and treatment results. EMR has been recognized as a valuable resource for large-scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation, and data reduction. For semistructured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (named-entity recognition) and RE (relation extraction). This paper focuses on the process of EMR processing and emphatically analyzes the key techniques. In addition, we make an in-depth study on the applications developed based on text mining together with the open challenges and research issues for future work.

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Li, Zhuo Shi, Ran Shi Jiang, and Jian Li. "The Application of Data Mining in the Honeypot System." Applied Mechanics and Materials 519-520 (February 2014): 189–92. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.189.

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Honeypot is a new type of active defense security technologies. This paper attempts to use of data mining methods to be mining and analysis of information collected on the honeypot system. Build a Windows system based on virtual machine technology research honeynet. Data collection be standardized and sequential pattern mining. Finding out the correlation between different data records and frequent with time-based sequence of audit data, which found that,select the law of value of the attack.

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Liang, Shi. "Data Mining and Economic Application in the Age of Financial Big Data: A Case Study of Shadow Banking and Interest Rate Liberalization in China." Mathematical Problems in Engineering 2022 (June 20, 2022): 1–8. http://dx.doi.org/10.1155/2022/9634999.

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With the booming of big data in finance, data mining technologies, as a new method of data statistics, have made superior economic applications available to researchers. Based on the relationship between shadow banking and interest rate liberalization, this paper intended to analyze the bidirectional relationship between shadow banking and interest rate liberalization using big data mining technologies. The relationship between data mining and the economic application of shadow banking was proven using data from 2014 to 2021. On this basis, this paper identified the bidirectional influence between shadow banking and interest rate liberalization. The findings show that shadow banking has positive contributions to the liberalization of interest rate, and the interest rate distortion resulting from interest rate control also drives the development of shadow banking. Moreover, feasible suggestions have been proposed for supervision on policies.

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