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Journal articles on the topic 'Data mining'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

PYLYPIUK, Tetiana, and Viktor SHCHYRBA. "DATA MINING METHODS." Collection of scientific papers Kamianets-Podilsky Ivan Ohienko National University Pedagogical series 29 (December 14, 2023): 7–10. http://dx.doi.org/10.32626/2307-4507.2023-29.7-10.

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Research is devoted to Data Mining methods. A comparison of classical and mathematical and statistical methods of data analysis was made. One of the variants of correlation analysis method for intelligent data analysis is proposed and described in an argumentative manner. The question of applying different methodologies for Data Mining is actual. Classically, the following methods of knowledge discovery and analysis are offered in Data Mining: classification; regression; forecasting time sequences (series); clustering; association. As mathematical and statistical methods of analysis in applied

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2

Shah Neha K, Shah Neha K. "Introduction of Data mining and an Analysis of Data mining Techniques." Indian Journal of Applied Research 3, no. 5 (2011): 137–39. http://dx.doi.org/10.15373/2249555x/may2013/41.

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Rakholiya, Kalpesh R., and Dr Dhaval Kathiriya. "Data Mining for Moving Object Data." Indian Journal of Applied Research 2, no. 3 (2011): 111–13. http://dx.doi.org/10.15373/2249555x/dec2012/34.

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Chomboon, K., N. Kaoungku, K. Kerdprasop, and N. Kerdprasop. "Data Mining in Semantic Web Data." International Journal of Computer Theory and Engineering 6, no. 6 (2014): 472–75. http://dx.doi.org/10.7763/ijcte.2014.v6.912.

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Загороднюк, П. А. "Data mining in Go." Vestnik of Russian New University. Series «Complex systems: models, analysis, management», no. 4 (January 10, 2022): 161–66. http://dx.doi.org/10.18137/rnu.v9187.21.04.p.161.

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Целью данной статьи является оценка языка программирования Go как инструмента для реализации методов data mining. Для этого проводится анализ задачи классификации и метода k-ближайших соседей, затем предлагается способ программирования данного метода и организации процесс управления и подготовки исходных данных. В заключение на основе проведенной работы делается вывод, насколько Go подходит для решения подобных задач, и есть ли потенциал для реализации остальных методов. The purpose of this article is to evaluate the Go programming language as a tool for implementing data mining methods. To do

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6

AVeselý. "Neural networks in data mining." Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (2012): 427–31. http://dx.doi.org/10.17221/5427-agricecon.

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To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining.

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Stoffel, Kilian. "Web + Data Mining = Web Mining." HMD Praxis der Wirtschaftsinformatik 46, no. 4 (2009): 6–20. http://dx.doi.org/10.1007/bf03340377.

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8

M., Inbavalli. "An Intelligent Agent based Mining Techniques for Distributed Data Mining." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 610–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201527.

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9

Tsuta, Mizuki. "Data Mining." Nippon Shokuhin Kagaku Kogaku Kaishi 64, no. 6 (2017): 334–35. http://dx.doi.org/10.3136/nskkk.64.334.

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10

Rossini, Luiz Amelio Sodaite, Renan Ricardo de Polli Silva, Eder Carlos Salazar Sotto, and Liriane Soares De Araújo. "DATA MINING." Revista Interface Tecnológica 15, no. 2 (2018): 50–59. http://dx.doi.org/10.31510/infa.v15i2.486.

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A Mineração de Dados (Data Mining) deve ser entendida como um conjunto de esforços empregados para a descoberta de padrões de acordo com bases de dados. Dessa maneira, há condições de gerar conhecimento útil para a tomada de decisões, através de algoritmos computacionais que recebem fatos do mundo real (entrada) e devolvem um padrão de comportamento (saída), expresso como modelagem de um perfil. Sendo assim, o objetivo deste artigo é definir a Mineração de Dados e os conceitos inerentes a ela, bem como elencar algumas ferramentas utilizadas para extração de conhecimento a partir dos dados. A m

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11

Nazem, Sufi M., and Bongsik Shin. "Data Mining." Journal of Database Management 10, no. 1 (1999): 39–42. http://dx.doi.org/10.4018/jdm.1999010104.

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12

Sharma, Anubhav. "Data Mining." International Journal for Research in Applied Science and Engineering Technology 9, no. 4 (2021): 953–56. http://dx.doi.org/10.22214/ijraset.2021.33790.

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13

Ziegel, Eric R., and Bhavani Thuraisingham. "Data Mining." Technometrics 42, no. 3 (2000): 327. http://dx.doi.org/10.2307/1271124.

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14

Alkadi, Ihssan. "Data Mining." Review of Business Information Systems (RBIS) 12, no. 1 (2008): 17–24. http://dx.doi.org/10.19030/rbis.v12i1.4394.

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Recently data mining has become more popular in the information industry. It is due to the availability of huge amounts of data. Industry needs turning such data into useful information and knowledge. This information and knowledge can be used in many applications ranging from business management, production control, and market analysis, to engineering design and science exploration. Database and information technology have been evolving systematically from primitive file processing systems to sophisticated and powerful databases systems. The research and development in database systems has le

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15

Kemp, Freda. "Data Mining." Journal of the Royal Statistical Society: Series A (Statistics in Society) 167, no. 1 (2004): 190–91. http://dx.doi.org/10.1111/j.1467-985x.2004.298_9.x.

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16

Lynch, Patrick K. "Data Mining." Biomedical Instrumentation & Technology 43, no. 1 (2009): 8. http://dx.doi.org/10.2345/0899-8205-43.1.8.

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17

Hand, David J. "Data Mining." Social Science Computer Review 18, no. 4 (2000): 442–49. http://dx.doi.org/10.1177/089443930001800407.

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18

Yongjian Fu. "Data mining." IEEE Potentials 16, no. 4 (1997): 18–20. http://dx.doi.org/10.1109/45.624335.

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19

O'Shea, Donald C. "Data Mining." Optical Engineering 37, no. 11 (1998): 2869. http://dx.doi.org/10.1117/1.601979.

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20

Olaru, C., and L. Wehenkel. "Data mining." IEEE Computer Applications in Power 12, no. 3 (1999): 19–25. http://dx.doi.org/10.1109/67.773801.

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21

Berzal, Fernando, and Nicolfás Matín. "Data mining." ACM SIGMOD Record 31, no. 2 (2002): 66–68. http://dx.doi.org/10.1145/565117.565130.

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22

Witten, Ian H., and Eibe Frank. "Data mining." ACM SIGMOD Record 31, no. 1 (2002): 76–77. http://dx.doi.org/10.1145/507338.507355.

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23

Ngo, Terry. "Data mining." ACM SIGSOFT Software Engineering Notes 36, no. 5 (2011): 51–52. http://dx.doi.org/10.1145/2020976.2021004.

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24

Hong, Se June. "Data mining." Future Generation Computer Systems 13, no. 2-3 (1997): 95–97. http://dx.doi.org/10.1016/s0167-739x(97)00014-9.

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25

Ozimek, John. "Data Mining." Journal of Database Marketing & Customer Strategy Management 10, no. 3 (2003): 280–81. http://dx.doi.org/10.1057/palgrave.jdm.3240117.

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26

METCALF, A. "DATA MINING." American Speech 75, no. 3 (2000): 237–39. http://dx.doi.org/10.1215/00031283-75-3-237.

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27

Meyer, Matthias. "Data mining." WIRTSCHAFTSINFORMATIK 48, no. 6 (2006): 454–55. http://dx.doi.org/10.1007/s11576-006-0106-y.

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28

Bissantz, Nicolas, and Jürgen Hagedorn. "Data Mining." Business & Information Systems Engineering 1, no. 1 (2008): 118–22. http://dx.doi.org/10.1007/s12599-008-0005-4.

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29

Cupples, L. Adrienne, Julia Bailey, Kevin C. Cartier, et al. "Data mining." Genetic Epidemiology 29, S1 (2005): S103—S109. http://dx.doi.org/10.1002/gepi.20117.

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30

Benoît, Gerald. "Data mining." Annual Review of Information Science and Technology 36, no. 1 (2005): 265–310. http://dx.doi.org/10.1002/aris.1440360107.

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31

Atif, Mohammad. "Data mining." International Journal of Communication and Information Technology 3, no. 1 (2022): 37–40. http://dx.doi.org/10.33545/2707661x.2022.v3.i1a.44.

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32

Trivedi, Nripesh. "Data mining." International Journal of Scientific Research and Management (IJSRM) 12, no. 03 (2024): 1094. http://dx.doi.org/10.18535/ijsrm/v12i03.ec07.

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Data Mining Data mining is about finding patterns in the data [1]. In this paper, I put forward an important insight about similarity in branches of computer science and data mining. All branches of computer science could be termed as a procedure to carry out data mining. In this paper, I detail that. The computer works by finding patterns in the input and output [2]. Artificial Intelligence works by finding the patterns of functions of the related variables [3]. Machine learning works by mathematical justification of machine learning methods and results [4]. That is the pattern followed in ma

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33

Rastogi, Mohit. "Spatial data mining features between general data mining." South Asian Journal of Marketing & Management Research 11, no. 11 (2021): 96–101. http://dx.doi.org/10.5958/2249-877x.2021.00116.8.

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34

Bathla, Gourav, Himanshu Aggarwal, and Rinkle Rani. "Migrating From Data Mining to Big Data Mining." International Journal of Engineering & Technology 7, no. 3.4 (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 st

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35

Raval, Hitesh R., and Dr Vikram Kaushik. "Data Mining: Performance Tuning Of Temporal Data Mining Based On Frequent Inter-Transaction Itemsets Discovery." International Journal of Scientific Research 3, no. 2 (2012): 78–82. http://dx.doi.org/10.15373/22778179/feb2014/25.

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36

Zvyagin, L. S. "DATA MINING: BIG DATA AND DATA SCIENCE." SOFT MEASUREMENTS AND COMPUTING 5, no. 54 (2022): 81–90. http://dx.doi.org/10.36871/2618-9976.2022.05.006.

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Data mining is the process of discovering information that can be used in large amounts of data. This method uses mathematical analysis, which helps to identify patterns and trends in the data. Such patterns cannot be noticed during normal data viewing due to the complexity of the relationships that arise with a large amount of data. All of them are a set of tools and methods that help humanity in the changing world around us. It is becoming more and more voluminous, we receive huge aggregates of data on various processes. Big Data and Data Science allow large companies to systematize informat

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37

Pragati, Sharma, and Sanjiv Sharma Dr. "DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGIES AND MANAGEMENT RESEARCH 5, no. 2 :SE (2018): 166–77. https://doi.org/10.5281/zenodo.1202113.

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Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining c

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38

Kriegel, Hans-Peter. "Data Science/Data Mining." Digitale Welt 3, no. 1 (2018): 7–8. http://dx.doi.org/10.1007/s42354-019-0141-7.

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39

W, Kiehn,. "From Big Data to Data Mining Von Big Data zu Data Mining." GIS Business 11, no. 6 (2016): 18–20. http://dx.doi.org/10.26643/gis.v11i6.5294.

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40

Yasuda, Akio. "Reviewing "Text Mining": Textual Data Mining." IEEJ Transactions on Electronics, Information and Systems 125, no. 5 (2005): 682–89. http://dx.doi.org/10.1541/ieejeiss.125.682.

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41

CHEN, ZHENGXIN. "FROM DATA MINING TO BEHAVIOR MINING." International Journal of Information Technology & Decision Making 05, no. 04 (2006): 703–11. http://dx.doi.org/10.1142/s0219622006002271.

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Knowledge economy requires data mining be more goal-oriented so that more tangible results can be produced. This requirement implies that the semantics of the data should be incorporated into the mining process. Data mining is ready to deal with this challenge because recent developments in data mining have shown an increasing interest on mining of complex data (as exemplified by graph mining, text mining, etc.). By incorporating the relationships of the data along with the data itself (rather than focusing on the data alone), complex data injects semantics into the mining process, thus enhanc

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42

Settino, Marzia, Alessandro Ruffolo, and Francesco La Regina. "“Mining” the sky from data mining." Acta Astronautica 59, no. 6 (2006): 499–502. http://dx.doi.org/10.1016/j.actaastro.2006.03.006.

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43

Wang, Lidong, and Guanghui Wang. "Data Mining Applications in Big Data." Computer Engineering and Applications Journal 4, no. 3 (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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44

Maheswari, R. Uma, S. Saravana Mahesan, Dr Tamilarasan, and A. K. Subramani. "Role of Data Mining in CRM." International Journal of Engineering Research 3, no. 2 (2014): 75–78. http://dx.doi.org/10.17950/ijer/v3s2/208.

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45

Anshu, Anshu. "Review Paper on Data Mining TechniquesandApplications." International Journal of Innovative Research in Computer Science & Technology 7, no. 2 (2019): 22–26. http://dx.doi.org/10.21276/ijircst.2019.7.2.4.

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46

Sherdiwala, Kainaz Bomi. "Data Mining Techniques in Stock Market." Indian Journal of Applied Research 4, no. 8 (2011): 327–29. http://dx.doi.org/10.15373/2249555x/august2014/82.

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47

Zodage, Kalyani Shahaji, Puja Sarage, Trupti Sudrik, and Rashmi Sonawane. "Health Prediction System Using Data Mining." Journal of Advances and Scholarly Researches in Allied Education 15, no. 2 (2018): 696–99. http://dx.doi.org/10.29070/15/56997.

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48

Pandey, Sachin. "Multilevel Association Rules in Data Mining." Journal of Advances and Scholarly Researches in Allied Education 15, no. 5 (2018): 74–78. http://dx.doi.org/10.29070/15/57517.

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49

S. Murali, S. Murali, C. B. Selvalakshmi C. B. Selvalakshmi, S. Padmadevi S. Padmadevi, and P. N. Karthikayan P. N. Karthikayan. "Data Mining Patters in Grid Computing." International Journal of Scientific Research 2, no. 3 (2012): 137–38. http://dx.doi.org/10.15373/22778179/mar2013/43.

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50

Hashmi, Faiz. "Elementary approach towards Biological Data Mining." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (2017): 1109–14. http://dx.doi.org/10.31142/ijtsrd7198.

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