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Journal articles on the topic 'Mining methods'

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Published: 4 June 2021
Last updated: 3 February 2022

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1

Singh, Sarah, and Ineke Klinge. "Mining for Methods." Freiburger Zeitschrift für GeschlechterStudien 21, no. 2 (November 9, 2015): 15–31. http://dx.doi.org/10.3224/fzg.v21i2.20934.

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2

., D. M. Kulkarni. "USING DATA MINING METHODS KNOWLEDGE DISCOVERY FOR TEXT MINING." International Journal of Research in Engineering and Technology 03, no. 01 (January 25, 2014): 24–29. http://dx.doi.org/10.15623/ijret.2014.0301005.

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3

VijayGaikwad, Sonali, Archana Chaugule, and Pramod Patil. "Text Mining Methods and Techniques." International Journal of Computer Applications 85, no. 17 (January 16, 2014): 42–45. http://dx.doi.org/10.5120/14937-3507.

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4

Rajavat, Anand, and Pranjal singh solanki. "Modern Association Rule Mining Methods." International Journal of Computational Science and Information Technology 2, no. 4 (November 30, 2014): 1–9. http://dx.doi.org/10.5121/ijcsity.2014.2401.

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5

Su, Xiaogang. "Data Mining Methods and Models." American Statistician 62, no. 1 (February 2008): 91. http://dx.doi.org/10.1198/tas.2008.s97.

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6

Booth, David E. "Data Mining Methods and Models." Technometrics 49, no. 4 (November 2007): 500. http://dx.doi.org/10.1198/tech.2007.s697.

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7

Chen, Liping, Jie Yang, and Wei Liu. "Global mining governance evaluation methods." Mineral Economics 28, no. 3 (November 2015): 123–27. http://dx.doi.org/10.1007/s13563-015-0073-0.

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8

Walker, S. "Comparative underground coal mining methods." Fuel and Energy Abstracts 37, no. 3 (May 1996): 170. http://dx.doi.org/10.1016/0140-6701(96)88326-3.

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9

Harper, Gavin, and Stephen D. Pickett. "Methods for mining HTS data." Drug Discovery Today 11, no. 15-16 (August 2006): 694–99. http://dx.doi.org/10.1016/j.drudis.2006.06.006.

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10

Rao, K. Srinivasa, and B. Srinivasa Rao. "An Insight in to Privacy Preserving Data Mining Methods." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 01, no. 02 (June 27, 2013): 31–35. http://dx.doi.org/10.9756/sijcsea/v1i2/0103570301.

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11

Kohli, Monika, and Rohit Tiwari. "Survey on Data Mining Related Methods / Techniques and Text Mining." IJARCCE 7, no. 8 (August 30, 2018): 15–18. http://dx.doi.org/10.17148/ijarcce.2018.783.

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12

Kononenko, Maksym, Oleh Khomenko, and Denys Astafiev. "New Сlassification of Ore Deposits Mining Methods." Advanced Engineering Forum 25 (November 2017): 71–79. http://dx.doi.org/10.4028/www.scientific.net/aef.25.71.

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The analysis of advantages and disadvantages of the existing classifications of mining method by the way of stoping space supporting in the course of extraction of ores is executed. The new classification of mining methods of ore deposits allowing to capture all range of the applied variants of systems for different mining-and-geological and mining conditions is developed. It is possible to formulate names of mining methods on proposed which allows to present a complex of the productions which are carried out during mining of production blocks.
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13

Huang, Xin, Hui Juan Chen, Mao Gong Zheng, Ping Liu, and Jing Qian. "Trajectory Pattern Mining: Methods and Applications." Applied Mechanics and Materials 490-491 (January 2014): 1361–67. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1361.

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With the advent of location-based social media and locationacquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. A lot of data mining algorithms have been successfully applied to trajectory data sets. Trajectory pattern mining has received a lot of attention in recent years. In this paper, we review the most inuential methods as well as typical applications within the context of trajectory pattern mining.
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14

Jan, Kalina. "Highly robust methods in data mining." Serbian Journal of Management 8, no. 1 (2013): 9–24. http://dx.doi.org/10.5937/sjm8-3226.

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15

Gigi, Ion-Trifoi. "Cost Calculation Methods In Mining Industry." Annales Universitatis Apulensis Series Oeconomica 1, no. 8 (June 1, 2006): 75–80. http://dx.doi.org/10.29302/oeconomica.2006.8.1.14.

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16

Chen, Lei-Da, Toru Sakaguchi, and Mark N. Frolick. "Data Mining Methods, Applications, and Tools." Information Systems Management 17, no. 1 (January 2000): 65–70. http://dx.doi.org/10.1201/1078/43190.17.1.20000101/31216.9.

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17

Solka, Jeffrey L. "Text Data Mining: Theory and Methods." Statistics Surveys 2 (2008): 94–112. http://dx.doi.org/10.1214/07-ss016.

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18

S, Sreekanth, and Dr Rao P.C. "Anomaly Detection Using Data Mining Methods." International Journal of Computer Trends and Technology 67, no. 12 (December 25, 2019): 20–23. http://dx.doi.org/10.14445/22312803/ijctt-v67i12p105.

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19

Wei, Wenge, and David W. Watkins. "Data mining methods for hydroclimatic forecasting." Advances in Water Resources 34, no. 11 (November 2011): 1390–400. http://dx.doi.org/10.1016/j.advwatres.2011.08.001.

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20

Kusiak, A. "Feature transformation methods in data mining." IEEE Transactions on Electronics Packaging Manufacturing 24, no. 3 (July 2001): 214–21. http://dx.doi.org/10.1109/6104.956807.

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21

Cios, K. J., W. Pedrycz, and R. M. Swiniarsk. "Data Mining Methods for Knowledge Discovery." IEEE Transactions on Neural Networks 9, no. 6 (November 1998): 1533–34. http://dx.doi.org/10.1109/tnn.1998.728406.

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22

Dasri, Yugandhara Bapurao, Bhagyashree Vyankatrao Barde, Nalwade Prakash Shivajirao, and Anant Madhavrao Bainwad. "Text Mining Framework, Methods and Techniques." IOSR Journal of Computer Engineering 19, no. 04 (July 2017): 19–22. http://dx.doi.org/10.9790/0661-1904021922.

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23

Nedic, Vladimir, Slobodan Cvetanovic, Danijela Despotovic, Milan Despotovic, and Sasa Babic. "Data mining with various optimization methods." Expert Systems with Applications 41, no. 8 (June 2014): 3993–99. http://dx.doi.org/10.1016/j.eswa.2013.12.025.

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24

Mansour, А. М., J. H. Mohammad, and Y. A. Kravchenko. "TEXT VECTORIZATION USING DATA MINING METHODS." IZVESTIYA SFedU. ENGINEERING SCIENCES, no. 2 (July 1, 2021): 154–67. http://dx.doi.org/10.18522/2311-3103-2021-2-154-167.

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25

Saraswathi Bai, V. "Data Mining Methods for Communication Technology." Asian Journal of Computer Science and Technology 8, S3 (June 5, 2019): 30–34. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2092.

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The aim of the project was to analyze the behavior of military communication networks based on work with real data collected continuously since 2005. With regard to the nature and amount of the data, data mining methods were selected for the purpose of analyses and experiments. The quality of real data is often insufficient for an immediate analysis. The article presents the data cleaning operations which have been carried out with the aim to improve the input data sample to obtain reliable models. Gradually, by means of properly chosen SW, network models were developed to verify generally valid patterns of network behavior as a bulk service. Furthermore, unlike the commercially available communication networks simulators, the models designed allowed us to capture non-standard models of network behavior under an increased load, verify the correct sizing of the network to the increased load, and thus test its reliability. Finally, based on previous experience, the models enabled us to predict emergency situations with a reasonable accuracy.
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26

Álvarez-Rodríguez, R., F. Pantoja-Timarán, and A. S. Rodríguez-Avelló. "Methods to reduce mercury pollution in small gold mining operations." Revista de Metalurgia 41, no. 3 (June 30, 2005): 194–203. http://dx.doi.org/10.3989/revmetalm.2005.v41.i3.205.

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27

Oswal, Prateek, and Divakar Singh. "Survey paper on various mining methods on multimedia Images." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 8, no. 3 (June 30, 2013): 898–901. http://dx.doi.org/10.24297/ijct.v8i3.3400.

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Multimedia mining is a young but challenging subfield in data mining .Multimedia explanation represents an application of computer vision that presents the recognition of objects or ideas related to a multimedia document as a image. There is not unified conclusion in the concept, content and methods of Multimedia mining, Multimedia mining architecture and framework has to be further studied. there are various mining methods that we can apply on multimedia images like association rule mining, sequence mining, sequence pattern mining etc. In this survey paper we are focusing all this methods. We also discussed feature selection methods of various images.
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28

Kaszowska, Olga, Piotr Gruchlik, and Wiesław Mika. "Industrial chimney monitoring - contemporary methods." E3S Web of Conferences 36 (2018): 01005. http://dx.doi.org/10.1051/e3sconf/20183601005.

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The paper presents knowledge acquired during the monitoring of a flue-gas stack, performed as part of technical and scientific surveillance of mining activity and its impact on industrial objects. The chimney is located in an area impacted by mining activity since the 1970s, from a coal mine which is no longer in existence. In the period of 2013-16, this area was subject to mining carried out by a mining entrepreneur who currently holds a license to excavate hard coal. Periodic measurements of the deflection of the 113-meter chimney are performed using conventional geodetic methods. The GIG used 3 methods to observe the stack: landbased 3D laser scanning, continuous deflection monitoring with a laser sensor, and drone-based visual inspections. The drone offered the possibility to closely inspect the upper sections of the flue-gas stack, which are difficult to see from the ground level.
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29

Zhao, Xiang Min, and Peng Yang. "Economic and Technical Analysis of Thick Coal Seam Mining Methods." Applied Mechanics and Materials 170-173 (May 2012): 872–75. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.872.

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Common mining methods for thick coal seams or extremely thick coal seams has slice mining top coal caving mining, greater height mining, and surface mining. From the perspective of economic and technological mining method, the choice of methods should be fully taken into account the technical, economic, quality of operating personnel, equipment, geological conditions. It allows priority to open-pit mining, and then according to the thickness, coal quality, coal other factors to select the top coal caving or mining of high extraction methods which can achieve the economic, security, and efficient purposes. Conversely, if the method is poor choice, it will certainly lead to economic and time losses.
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30

Zuzik, Jozef, Roland Weiss, Erik Weiss, Slavomir Labant, and Ladislav Mixtaj. "Evaluation of companies by revenues methods in the conditions of Slovakia." Problems and Perspectives in Management 15, no. 3 (October 5, 2017): 16–23. http://dx.doi.org/10.21511/ppm.15(3).2017.02.

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Goal of the contribution is to create universal model for evaluation of companies, acting in mining industry in Slovakia. Model is orientated to the companies, acting in mining industry in Slovakia and it consists from several logically relating steps. Also application of suggested process in chosen mining company is made as well as achieved results are mutually compared. Attention is given to the revenue methods with necessity of financial plan, as well as methods that do not demand knowledge of financial plan.
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31

Lesin, Yury, Vyacheslav Gogolin, Elena Murko, Sergey Markov, and Jurgen Kretschmann. "The Choice of Methods of Quarry Wastewater Purifying." E3S Web of Conferences 41 (2018): 01039. http://dx.doi.org/10.1051/e3sconf/20184101039.

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The negative effect made by mining enterprises on the natural environment has complex origination. The intensive pollution is observed because of the influence of mineral deposits open-pit mining on the atmosphere, water resources and landscape complexes. Complex processes of environmental anthropogenic changes caused by open pits’ operations have brought to light the problem of surface water pollution near large mining segments. Industrial wastewater of mining enterprises has a significant impact on the natural environment. In connection with the continuous and significant increase in the volume of mining, the amount of wastewater from mines, quarries and processing plants is constantly increasing. The main components of wastewater from operating mining enterprises are mine (quarry) waters, as well as runoff from atmospheric waters polluted by water erosion of dumps and mineral stacks. The paper describes the possible ways of quarry wastewater purifying – using hydrocyclones and artificial filtering arrays made from overburden rock.
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32

ÁDÁM, Norbert, Branislav MADOŠ, Marek ČAJKOVSKÝ, Ján HURTUK, and Tomáš TOMČÁK. "Methods of the Data Mining and Machine Learning in Computer Security." Acta Electrotechnica et Informatica 14, no. 2 (June 1, 2014): 46–50. http://dx.doi.org/10.15546/aeei-2014-0017.

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33

Natarajan, Jeyakumar. "Text Mining Perspectives in Microarray Data Mining." ISRN Computational Biology 2013 (November 5, 2013): 1–5. http://dx.doi.org/10.1155/2013/159135.

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Current microarray data mining methods such as clustering, classification, and association analysis heavily rely on statistical and machine learning algorithms for analysis of large sets of gene expression data. In recent years, there has been a growing interest in methods that attempt to discover patterns based on multiple but related data sources. Gene expression data and the corresponding literature data are one such example. This paper suggests a new approach to microarray data mining as a combination of text mining (TM) and information extraction (IE). TM is concerned with identifying patterns in natural language text and IE is concerned with locating specific entities, relations, and facts in text. The present paper surveys the state of the art of data mining methods for microarray data analysis. We show the limitations of current microarray data mining methods and outline how text mining could address these limitations.
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34

Sahoo, Anoop J., and Yugal Kumar. "Seminal quality prediction using data mining methods." Technology and Health Care 22, no. 4 (August 1, 2014): 531–45. http://dx.doi.org/10.3233/thc-140816.

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35

Stefanowski, Jerzy. "Combined learning methods and mining complex data." Intelligent Data Analysis 16, no. 5 (October 8, 2012): 741–43. http://dx.doi.org/10.3233/ida-2012-0548.

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36

Bidyuk, P. I., and V. H. Huskova. "Analysis of Solvency Using Data Mining Methods." Èlektronnoe modelirovanie 41, no. 2 (April 11, 2019): 111–20. http://dx.doi.org/10.15407/emodel.41.02.111.

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37

Wang, Hai Feng, Hong E. Ren, Kun Zhang, and Hong Xu Wang. "Mining Methods Based on Vague Optimization Evaluation." Advanced Materials Research 659 (January 2013): 128–33. http://dx.doi.org/10.4028/www.scientific.net/amr.659.128.

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Method based on vague optimization evaluation is vague pattern recognition. There are six detailed steps of application. The first, Set up Techno-economic indicator system. Secondly set up preparative optimization scheme sets. Thirdly set up optimal scheme in theory. It is made up of each Techno-economic indicator optimal data. Fourthly transform techno-economic input data into vague data. The fifth, Calculating similarly measures. Similarity measures will be evaluated between preparative optimization scheme vague sets and optimal scheme in theory. The last is vague optimization evaluation. The weight of each preparative optimization scheme is given. The data of weighted similarity measures by the weight factors are obtained. And applying them we obtain the good and bad sort of vague optimization scheme. The new similarity measures formula between vague sets is given. The formula is indispensable in the method of vague optimization evaluation. Application examples show that the Vague optimization evaluation method to the conclusion is reliable.
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38

He, Yue Shun, and Jun Fang Xiao. "Improved Methods on Association Rules Mining Algorithms." Key Engineering Materials 460-461 (January 2011): 148–52. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.148.

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Among the many mining algorithms of association rules, Apriori Algorithm is a classical algorithm that has caused the most discussion; it can effectively carry out the mining association rules. However, based on Apriori Algorithm, most of the traditional algorithms exist "item sets generation bottleneck" problem, and are very time-consuming. An enhanced algorithm associating Apriori with transaction reduction and item reduction technique is put forward by the paper, in the algorithm candidate item sets generation and the support calculation are created after each transaction is compressed and connected, and the key word identifying is adopted in the candidate set, thus the process of pruning and string pattern matching is removed from Apriori algorithm. Original algorithm and improved algorithm implementation steps are presented by examples, the results show that the new algorithm reduces the storage space, improve the efficiency of the algorithm and improve the performance of data mining technology.
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39

Santibáñez, Francisco, Carlos Flores, Franco Basso, Abelino Jiménez, Francisco Bravo, Felipe Núñez, Héctor Luco, Luis Martínez, and ángel Benítez. "Mining Accident Detection Using Machine Learning Methods." IFAC Proceedings Volumes 46, no. 16 (2013): 31–33. http://dx.doi.org/10.3182/20130825-4-us-2038.00051.

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40

Shevchenko, Aleksei, and Vitaly Khramovskykh. "On application prospects of automated mining methods." Proceedings of the Siberian Department of the Section of Earth Sciences of the Russian Academy of Natural Sciences. Geology, Exploration and Development of Mineral Deposits 42, no. 1 (March 2019): 104–11. http://dx.doi.org/10.21285/2541-9455-2019-42-1-104-111.

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41

Stephanopoulos, Gregory, Georg Locher, and Michael Duff. "Pattern Recognition Methods for Fermentation Database Mining." IFAC Proceedings Volumes 28, no. 3 (May 1995): 195–98. http://dx.doi.org/10.1016/s1474-6670(17)45625-4.

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42

Vandamme, J. ‐P, N. Meskens, and J. ‐F Superby. "Predicting Academic Performance by Data Mining Methods." Education Economics 15, no. 4 (December 2007): 405–19. http://dx.doi.org/10.1080/09645290701409939.

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43

Edwards, David. "Data Mining: Concepts, Models, Methods, and Algorithms." Journal of Proteome Research 2, no. 3 (June 2003): 334. http://dx.doi.org/10.1021/pr030789n.

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44

SRIVASTAVA, ASHOK N. "Data Mining: Concepts, Models, Methods, and Algorithms." Journal of Computing and Information Science in Engineering 5, no. 4 (December 1, 2005): 394–95. http://dx.doi.org/10.1115/1.2123107.

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45

Gilbert, Catherine. "XML data mining: models, methods, and applications." Australian Library Journal 62, no. 3 (August 2013): 252–53. http://dx.doi.org/10.1080/00049670.2013.811779.

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46

Niti Desai, Niti Desai. "Sequential Pattern Mining Methods: A Snap Shot." IOSR Journal of Computer Engineering 10, no. 4 (2013): 12–20. http://dx.doi.org/10.9790/0661-01041220.

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47

Carenini, Giuseppe, Gabriel Murray, and Raymond Ng. "Methods for Mining and Summarizing Text Conversations." Synthesis Lectures on Data Management 3, no. 3 (June 25, 2011): 1–130. http://dx.doi.org/10.2200/s00363ed1v01y201105dtm017.

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48

Hernest, Mircea-Dan. "Light monotone Dialectica methods for proof mining." MLQ 55, no. 5 (October 2009): 551–61. http://dx.doi.org/10.1002/malq.200710093.

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49

Liang, Ming. "Data Mining: Concepts, Models, Methods, and Algorithms." IIE Transactions 36, no. 5 (May 2004): 495–96. http://dx.doi.org/10.1080/07408170490426107.

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50

Ibrahim, Sara K., Ayman Ahmed, M. Amal Eldin Zeidan, and Ibrahim E. Ziedan. "Machine Learning Methods for Spacecraft Telemetry Mining." IEEE Transactions on Aerospace and Electronic Systems 55, no. 4 (August 2019): 1816–27. http://dx.doi.org/10.1109/taes.2018.2876586.

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