Journal articles: 'Mining'

1

Seely Brown, John. "Minding and Mining the Periphery." Long Range Planning 37, no. 2 (April 2004): 143–51. http://dx.doi.org/10.1016/j.lrp.2004.01.001.

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2

Mudrooroo. "Mining the Earth without Minding the Earth." World Literature Today 67, no. 3 (1993): 569. http://dx.doi.org/10.2307/40149371.

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Nagasivaanjaneya Reddy R, Sunkara. "Web Mining." International Journal of Science and Research (IJSR) 12, no. 3 (March 5, 2023): 900–903. http://dx.doi.org/10.21275/sr23303173846.

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4

Wang, Pu. "Research on the Mechanism of Web Data Mining in Electronic Commerce Application." Applied Mechanics and Materials 687-691 (November 2014): 3003–6. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3003.

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At present, the growth of the Internet has brought us a vast amount of information that we can hardly deal with. To solve the flood of information, various data mining systems have been created to assist and augment this natural social process. Data minig recommender systems have been developed to automate the recommendation process. Data mining recommender systems can be found at many electronic commerce applications. In this paper, a recommendation mechanism of web data mining in electronic commerce application is given. Then, presents the workflow of the web data mining in electronic commcer. Lastly, the usage of the tools of web data mining is described.

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SAWADA, Kenji. "Mining Information and Mining Policy." Shigen-to-Sozai 120, no. 9 (2004): 532–34. http://dx.doi.org/10.2473/shigentosozai.120.532.

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Jelen, Jakub. "Mining Heritage and Mining Tourism." Czech Journal of Tourism 7, no. 1 (June 1, 2018): 93–105. http://dx.doi.org/10.1515/cjot-2018-0005.

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Abstract The mining industry leaves behind a large number of tangible or intangible remains. A part of them can be used by different actors as a mining heritage. Specific cultural or social values and meanings, which remind of the importance of mining in the past and today, are represented through this mining heritage. To preserve them, the heritage must be passed to future generations. One of the ways is mining tourism. However, the relation between the mining heritage and tourism is not simple as the complex mining heritage has many specific features that differ from the traditional tourist attractions and conceal many pitfalls. The aspects of authenticity or approaches to heritage interpretation are also important.

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7

Murray, L. "The mining pioneers [mining - Finland]." Engineering & Technology 17, no. 10 (November 1, 2022): 60–63. http://dx.doi.org/10.1049/et.2022.1005.

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8

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

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9

Choi, Seung Joo, and Jong-bae Kim. "Comparison of Mining Capability between ASIC Mining and GPU Mining." Asia-pacific Journal of Multimedia services convergent with Art, Humanities, and Sociology 7, no. 5 (May 31, 2017): 829–36. http://dx.doi.org/10.14257/ajmahs.2017.05.03.

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M., Inbavalli. "An Intelligent Agent based Mining Techniques for Distributed Data Mining." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 610–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201527.

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SINAGA, MARKUSDIAN, and ACHMAD ZAINI. "PERBANDINGAN PENDAPATAN USAHATANI PADI SAWAH (Oryza sativa L.) DI DALAM DAN DI LUAR AREAL TAMBANG (Studi Kasus Areal Tambang PT. Multi Harapan Utama di Desa Jonggon Jaya Kecamatan Loa Kulu) (The Income Comparison of Wetland Paddy Farming (Oryza sativa L.) in Inside and Outside of Mining Area (Case Study Mining Area of PT. Multi Harapan Utama in Jonggong Jaya Village Loa Kulu Subdistrict))." JURNAL AGRIBISNIS DAN KOMUNIKASI PERTANIAN (Journal of Agribusiness and Agricultural Communication) 3, no. 1 (April 20, 2020): 57. http://dx.doi.org/10.35941/jakp.3.1.2020.3531.57-62.

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The purposes of this research were to determine the income of wetland paddy farming in inside and outside of mining area and to compare both of them. This research was conducted from August to October 2018 in minning area of PT. Multi Harapan Utama, Jonggon Jaya Village, Loa Kulu Subdistrict. Data collection was done through direct interview toward 33 respondents. Data was analyzed to count income and to do t test. The result of this research showed that the average income of wetland paddy farming in inside mining area was IDR12,049,299.99 ha-1ps-1 while the average income of wetland paddy farming in outside mining area was IDR10,663,654.70 ha-1ps-1. Income of wetland paddy farming in inside mining area was bigger than that in outside mining area based on tcount > ttable (3.637 > 2.039).

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Gorzala, Jeannette, and Martin Hanzl. "Mining." Zeitschrift für das gesamte Bank- und Börsenwesen 66, no. 8 (2018): 560. http://dx.doi.org/10.47782/oeba201808056001.

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13

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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14

CHEN, ZHENGXIN. "FROM DATA MINING TO BEHAVIOR MINING." International Journal of Information Technology & Decision Making 05, no. 04 (December 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 enhancing the potential of making better contribution to knowledge economy. Since the relationships between the data reveal certain behavioral aspects underlying the plain data, this shift of mining from simple data to complex data signals a fundamental change to a new stage in the research and practice of knowledge discovery, which can be termed as behavior mining. Behavior mining also has the potential of unifying some other recent activities in data mining. We discuss important aspects on behavior mining, and discuss its implications for the future of data mining.

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

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16

Jang, Myounghwan. "A Study on Mining Design and Mining Method of Retreat Mining." Journal of the Korean Society of Mineral and Energy Resources Engineers 57, no. 1 (February 1, 2020): 127–36. http://dx.doi.org/10.32390/ksmer.2020.57.1.127.

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17

Ben-Awuah, Eugene, Otto Richter, Tarrant Elkington, and Yashar Pourrahimian. "Strategic mining options optimization: Open pit mining, underground mining or both." International Journal of Mining Science and Technology 26, no. 6 (November 2016): 1065–71. http://dx.doi.org/10.1016/j.ijmst.2016.09.015.

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18

Zhang, Jixiong, Qiang Zhang, A. J. S. (Sam) Spearing, Xiexing Miao, Shuai Guo, and Qiang Sun. "Green coal mining technique integrating mining-dressing-gas draining-backfilling-mining." International Journal of Mining Science and Technology 27, no. 1 (January 2017): 17–27. http://dx.doi.org/10.1016/j.ijmst.2016.11.014.

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19

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 research, the most of authors offer such methods as: statistical hypothesis testing, regression models construction and research. Since most real models are not amenable to analysis using classical methods, including regression analysis, the authors propose to use correlational analysis method in Data Mining.

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20

Тихонова, Elena Tikhonova, Трещевская, Ella Treshchevskaya, Панков, Yakov Pankov, Малинина, and Tatyana Malinina. "Chemical composition of vegetation in man-made landscapes Kursk magnetic anomaly." Forestry Engineering Journal 6, no. 3 (October 10, 2016): 48–53. http://dx.doi.org/10.12737/21679.

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The open-mining leads to the formation of the post-miningo or disturbed lands, which are characterized by extreme conditions. The largest area of the post-mining and disturbed lands in Central Black Soil Region are situated near the Kursk magnetic anomaly enterprises. The main direction of the restoration of the post-mining lands is the forestry reclamation. The biocenosis contribute to changes of substrates properties and their transformation into primary soil (“em-bryonic soils”). The dumps vegetation absorbs nitrogen, ash elements and heavy metals from the substrate and air and returns the above-mentioned elements with litter.

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21

Ali, Muhammad Rajif. "TANGGAPAN MASYARAKAT TENTANG HADIRNYA PERTAMBANGAN ASPAL DI DESA WINNING KECAMATAN PASARWAJO KABUPATEN BUTON." Jurnal Penelitian Pendidikan Geografi 3, no. 2 (April 1, 2018): 157. http://dx.doi.org/10.36709/jppg.v3i2.9156.

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This study aims to describe the public perception of the existence of asphalt minning in relation to the environmental inpact caused in the Winning Vilage of Pasar wajo Distric in Buton District. This research is descriptive qualitative whit case study approach. Data collection is done by using method: questionare, interview, observation and documentation. Data obtained from 55 respondents, then presented in tabular from percentage, then described qualitatively. The result showed that the public response about the presence of asphalt mining in Winning Vilage, divided into positive and negative impacts. Positive impacts of asphalt mining such as: increased local income, creation of employment opportunities for local communities, exposure of territories from isolason. While the negative impact is asphalt mining activities caused environmental damage include: 1) land danmage, land disruption, 2) Damage to flora and fauna, communities responding to asphalt mining activities caused damage to vegetation and people responded to asphalt mining activities causing damage/wild animal/wildlife, 3) The contamination river water, times and swamps where people respond to the mining activitie of asphalt cause pollution of riverwater/times and swamps, 4) Air pollution mainly dust produced from the development process and the means of transportation passing by, 5) Noise, where people respount to asphalt mining activities generate noise, 6) The existence of healt problems where people respoud to asphalt mining activities cause public health problems,

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22

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 (October 1, 2011): 137–39. http://dx.doi.org/10.15373/2249555x/may2013/41.

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23

Neha K, Shah. "Introduction of Text mining and an Analysis of Text mining Techniques." Paripex - Indian Journal Of Research 2, no. 2 (January 15, 2012): 56–57. http://dx.doi.org/10.15373/22501991/feb2013/18.

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24

-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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25

Singh, Priya, and Ajit Kumar Behura. "Feminist Mining: A Step Towards Sustainable Mininig in India." Problemy Ekorozwoju 17, no. 1 (January 1, 2022): 235–45. http://dx.doi.org/10.35784/pe.2022.1.21.

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Economic growth and development with least harm to the environment is one of the biggest challenges for the human. Mining which is considered to be the most inherently unsustainable industry, is at the same time, plays key role in the development process in India and across Globe. It occupy primary position in the supply chain, contributing to the foreign direct investment, exports, government taxes, GDP (National Income) and provide huge labour employment. This paper studies industrial elimination of women, prolong industrial patriarchy, impetuous production, rising demand pressure has added to the quadrupling problems of land degradation, disposal of over burden/waste discharge, deforestation, pollution(air, water, and noise), and damage to forest flora and fauna, occupational health hazards and extreme global environmental damage in a rapid, continuous manner. These continuous and multi-layered problems are giving us reason to re-visioning steps, where we went wrong. Theoretical foundation of eco-feminism traces that negligence and suppression of women in general and in the mining industry, is one of the probable cause that hit environment fiercely. With this in mind, this paper suggests theory of eco-feminism into male centric mining industry as a perspective which is more likely to lead to industrial sustainability, making it more responsible and may provide pro-environmental solutions to the industry. This feminist concept extending it from individualistic level into mining organisations proposes as one of the alternative to combine ecology and commerce to envision sustainability.

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26

Wickremasinghe, W. N. "Mining for treasures of information: data mining." Ceylon Journal of Medical Science 45, no. 1 (June 23, 2002): 39. http://dx.doi.org/10.4038/cjms.v45i1.4861.

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27

S, Vishesh, Manu Srinath, Supriya Ramarao Prasanna, and Supriya Yadati Narasimhulu. "Data Mining, Internet Marketing and Web Mining." IJARCCE 6, no. 3 (March 30, 2017): 507–9. http://dx.doi.org/10.17148/ijarcce.2017.63117.

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28

C, Christys, and Arivalagan S. "Text Mining Techniques in Data Mining – Review." International Journal of Data Mining Techniques and Applications 7, no. 1 (March 15, 2018): 181–85. http://dx.doi.org/10.20894/ijdmta.102.007.001.029.

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29

Brueckner, Martin, Rochelle Spencer, Sally Knowles, and Megan Paull. "Mining legacies––Broadening understandings of mining impacts." Extractive Industries and Society 8, no. 3 (September 2021): 100950. http://dx.doi.org/10.1016/j.exis.2021.100950.

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30

Monteiro, Nathalie Barbosa Reis, Ana Keuly Luz Bezerra, José Machado Moita Neto, and Elaine Aparecida da Silva. "Mining Law: In Search of Sustainable Mining." Sustainability 13, no. 2 (January 16, 2021): 867. http://dx.doi.org/10.3390/su13020867.

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Mining is an activity that generates inputs to different production chains, making it essential for any country’s development. However, it causes environmental, economic, and social impacts that must be considered. The Mining Law provides guidelines, through laws and regulations, so the activity can be carried out in an environmentally, economically, and socially sustainable way. In this paper, an analysis was conducted of the application of some peculiar characteristics of Brazilian Mining Law (locational rigidity, priority granting, among others) according to the parameters established in the Federal Constitution, in the Mining Code and its updates, and in the National Department of Mineral Production, Environment Ministry, and Mines and Energy Ministry normative acts. Moreover, the Superior Court of Justice Jurisprudence was analyzed to understand how the Mining Law is applied, in practice. It was verified that the Brazilian legislation is not perfect, but it has mechanisms to protect and benefit the miner, the society, and the environment. However, there are many shortcomings like the lack of speed in judging processes and the flaws in the applicability of some principles that compromise sustainability in the activity’s development, especially regarding the environmental and social liabilities left after mine closure.

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Kislyakov, V. Е., and P. V. Katyshev. "SURFACE MINING FAN-SHAPED MINING METHOD INVESTIGATION." Ugol’, no. 06 (June 8, 2017): 11–16. http://dx.doi.org/10.18796/0041-5790-2017-6-11-16.

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32

Williams, L. "Sustainability - Mining. Can mining ever go green?" Engineering & Technology 15, no. 1 (February 1, 2020): 48–51. http://dx.doi.org/10.1049/et.2020.0106.

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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

Neale, Timothy, and Eve Vincent. "Mining, indigeneity, alterity: or, mining Indigenous alterity?" Cultural Studies 31, no. 2-3 (April 25, 2017): 417–39. http://dx.doi.org/10.1080/09502386.2017.1303435.

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35

Baena-Garcı´a, M., and R. Morales-Bueno. "Mining interestingness measures for string pattern mining." Knowledge-Based Systems 25, no. 1 (February 2012): 45–50. http://dx.doi.org/10.1016/j.knosys.2011.01.013.

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36

Nijat Babayev, Nijat Babayev. "WEB MINING: DATA MINING ON THE INTERNET." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 23, no. 12 (December 19, 2022): 182–93. http://dx.doi.org/10.36962/pahtei23122022-182.

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In the article“Web Mining: Data Mining on the Web” 3 different components of Web Mining have been described and mainly Web Usage Mining has been discussed in detail and analyzed with examples. The general theme of the article is clarified by giving such sub-topics: Difficulties in analyzing data from the Internet, Stages of Web-Mining, Analysis of the use of Web resources (Web Usage Mining), Web Server Log Files and etc. General Relationships Between Web Mining Categories and Data Mining Tasks are shown. At the end, in the Conclusion, it is shown that to solve such problems, Data Mining technology is used. This name refers to a set of methods that allow us to extract useful information according to certain rules from the amount of collected data in which this information is implicitly contained. In this case, both statistical and intellectual processing methods are used. Keywords: Web-Mining, Data Mining Methods, Data from the Internet, Web Usage Mining, User Session Identification.

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37

Wu, Xin-Dong, Xing-Quan Zhu, Qi-Jun Chen, and Fei-Yue Wang. "Ubiquitous Mining with Interactive Data Mining Agents." Journal of Computer Science and Technology 24, no. 6 (November 2009): 1018–27. http://dx.doi.org/10.1007/s11390-009-9291-7.

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38

Harit Priyadarshi, Nasim, B. N. Dubey, Meenali Modi. "Green Mining: Environmental Ethics in Mining Engineering." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (October 28, 2023): 3099–104. http://dx.doi.org/10.52783/tjjpt.v44.i3.1286.

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Green mining, an evolving paradigm within mining engineering, is driven by a commitment to environmental ethics and sustainability. This article explores the principles, benefits, and challenges associated with green mining, presenting a comprehensive view of its potential transformation of the mining industry. Traditional mining practices have historically resulted in habitat destruction, air and water pollution, and other environmental challenges. However, green mining principles emphasize resource efficiency, minimal environmental disturbance, responsible waste management, and social responsibility. The adoption of these principles promises numerous advantages, including environmental conservation, reduced carbon footprint, economic efficiency, and long-term sustainability. Despite various challenges, such as economic pressures and regulatory hurdles, the transition to green mining is gaining momentum. This shift embodies an ethical imperative and a practical approach to ensuring responsible resource extraction while preserving the planet's delicate ecosystems and the well-being of local communities.

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39

Kutina, Jan. "U.S. Mining in a global context - Comments on the Mining Law Reform." Global Tectonics and Metallogeny 6, no. 1 (August 1, 1996): 43–56. http://dx.doi.org/10.1127/gtm/6/1996/43.

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40

Aliya Bukayeva. "Globalization in mining. Global, regional, local mining review. Comparative analysis with Kazakhstan mining." Asia-Pacific Journal of Business Venturing and Entrepreneurship 5, no. 1 (March 2010): 81–91. http://dx.doi.org/10.16972/apjbve.5.1.201003.81.

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41

Stoica, Eduard Alexandru, and Esra Kahya Özyirmidokuz. "Mining Customer Feedback Documents." International Journal of Knowledge Engineering-IACSIT 1, no. 1 (2015): 68–71. http://dx.doi.org/10.7763/ijke.2015.v1.12.

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42

D, Monisha, and Arul Kumar. "Mining High Utility Dataset." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 2136–43. http://dx.doi.org/10.31142/ijtsrd11691.

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43

Barteld, Fabian, and Alexander Ziem. "Construction mining." Belgian Journal of Linguistics, Volume 34 (2020) 34 (December 31, 2020): 5–16. http://dx.doi.org/10.1075/bjl.00030.bar.

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Abstract The German Constructicon Project (www.german-constructicon.de) aims at documenting grammatical constructions in contemporary standard German on the basis of annotated corpus examples, including relations between constructions and between constructions and evoked semantic frames. So far, the research focus has been mainly on the development and computational implementation of a constructicographic workflow (including a parsing pipeline) that allows for addressing any kind of constructions on varying levels of schematicity, idiomaticity, and abstractness. However, such an exemplar-driven procedure precludes us from systematically identifying constructional candidates. In this article, we scrutinize ways to operationalize and implement data-mining procedures to inductively identify construction candidates.

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44

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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Kassymkanova, H., G. Jangulova, E. Zhalgasbekov, and G. Baidauletova. "Mining waste." Journal of Geography and Environmental Management 40, no. 1 (2015): 172–75. http://dx.doi.org/10.26577/jgem.2015.1.178.

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46

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 (December 30, 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 metodologia do trabalho consiste em levantamento bibliográfico. Espera-se como resultado demonstrar a imprescindibilidade da Mineração de Dados no apoio à decisão nas organizações e contribuir para a produção científica e acadêmica.

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47

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

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48

Sharma, Anubhav. "Data Mining." International Journal for Research in Applied Science and Engineering Technology 9, no. 4 (April 30, 2021): 953–56. http://dx.doi.org/10.22214/ijraset.2021.33790.

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49

Jelenkovic, Rade, and Radule Tosovic. "Mining royalties." Tehnika 69, no. 3 (2014): 416–23. http://dx.doi.org/10.5937/tehnika1403416j.

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50

Godoy, Ricardo A. "Bolivian Mining." Latin American Research Review 20, no. 1 (1985): 272–77. http://dx.doi.org/10.1017/s0023879100034439.

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Journal articles on the topic 'Mining'

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