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Journal articles on the topic 'Mining statistics'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Hand, David J. "Statistics and data mining." ACM SIGKDD Explorations Newsletter 1, no. 1 (1999): 16–19. http://dx.doi.org/10.1145/846170.846171.

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2

Attwal, Kanwal Preet Singh, and Amardeep Singh Dhiman. "Exploring SPSS statistics for data mining and statistical modeling." International Journal of Statistics and Applied Mathematics 9, no. 3 (2024): 09–16. http://dx.doi.org/10.22271/maths.2024.v9.i3a.1721.

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3

Jin, Taolan, Bo Zhang, and Zhi Yang. "Cloud Statistics of Accounting Informatization Based on Statistics Mining." Computational Intelligence and Neuroscience 2022 (August 27, 2022): 1–10. http://dx.doi.org/10.1155/2022/3493678.

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With the rapid development of information technology, the amount of all kinds of data information is increasing rapidly. As an important means to collect, store, and manage massive data, and then analyze and predict the habits and characteristics of certain groups of people and even the development trend of a certain industry, big data technology provides a comprehensive strategic basis for management decision makers that the traditional processing mode cannot match. Contemporary management accounting serves the whole process of enterprise internal control, so it will produce a large number of

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4

Hand, David J. "Data Mining: Statistics and More?" American Statistician 52, no. 2 (1998): 112. http://dx.doi.org/10.2307/2685468.

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5

Hand, David J. "Data Mining: Statistics and More?" American Statistician 52, no. 2 (1998): 112–18. http://dx.doi.org/10.1080/00031305.1998.10480549.

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6

Hofmann, Heike, Antony Unwin, and Adalbert Wilhem. "Data Mining and Statistics — Introduction." Computational Statistics 16, no. 3 (2001): 317–21. http://dx.doi.org/10.1007/s001800100069.

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7

MacLaren, Graeme, James D. Fortenberry, and Heidi J. Dalton. "Lies, Statistics, and ECMO Data Mining." Pediatric Critical Care Medicine 17, no. 8 (2016): 799–802. http://dx.doi.org/10.1097/pcc.0000000000000870.

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8

Zhao, Chun-Mei, and Jing Luan. "Data mining: Going beyond traditional statistics." New Directions for Institutional Research 2006, no. 131 (2006): 7–16. http://dx.doi.org/10.1002/ir.184.

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9

Banks, David L. "Statistical data mining." WIREs Computational Statistics 2, no. 1 (2009): 9–25. http://dx.doi.org/10.1002/wics.53.

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10

Intelligence and Neuroscience, Computational. "Retracted: Cloud Statistics of Accounting Informatization Based on Statistics Mining." Computational Intelligence and Neuroscience 2023 (August 2, 2023): 1. http://dx.doi.org/10.1155/2023/9836184.

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11

Xiao, Guobao, Shiping Wang, Han Wang, and Jiayi Ma. "Mining consistent correspondences using co-occurrence statistics." Pattern Recognition 119 (November 2021): 108062. http://dx.doi.org/10.1016/j.patcog.2021.108062.

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12

Gözde KILIÇ, Güner. "Data Mining and Statistics in Data Science." Social Sciences Studies Journal 5, no. 30 (2019): 960–68. http://dx.doi.org/10.26449/sssj.1295.

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13

Chattopadhyay, Molly, and Anupam Lahiri. "Data anomaly in mining statistics of India." Statistical Journal of the IAOS 33, no. 2 (2017): 547–56. http://dx.doi.org/10.3233/sji-160281.

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14

Moriya, Koichi, Shin Matsushima, and Kenji Yamanishi. "Traffic Risk Mining From Heterogeneous Road Statistics." IEEE Transactions on Intelligent Transportation Systems 19, no. 11 (2018): 3662–75. http://dx.doi.org/10.1109/tits.2018.2856533.

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15

Kuptsova, I. S., Y. I. Bulygin, R. R. Lazurenko, and A. R. Temirkanov. "Predicting accidents in the mining industry in Zimbabwe in order to develop preventive measures to reduce them." SAFETY OF TECHNOGENIC AND NATURAL SYSTEMS, no. 4 (2020): 2–11. http://dx.doi.org/10.23947/2541-9129-2020-4-2-11.

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Introduction. Industrial accidents are associated with various factors: human, social and economic problems, which we must strive to eliminate, thereby ensuring a safe working environment. Statistics on occupational injuries are necessary to assess the degree of hazard and potential risks associated with occupational factors to protect workers. Labor inspectorate statistics play an important role in developing national policies, systems, programs and strategies to improve safety and working conditions for miners in Zimbabwe. Labor Inspection in Zimbabwe is one of the main safety control mechan

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16

Penner, Ralph, and Donald G. Watts. "Mining Information." American Statistician 45, no. 1 (1991): 4. http://dx.doi.org/10.2307/2685231.

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17

Adams, Niall M. "Perspectives on Data Mining." International Journal of Market Research 52, no. 1 (2010): 11–19. http://dx.doi.org/10.2501/s147078531020103x.

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As a data analysis technology, data mining has matured to the extent that there are now a number of sophisticated commercial software packages available. The purpose of this article is to explore what data mining has become, its relationship to statistics and its relevance in market research.

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18

Woodward, Phil, and Sam Woodward. "Mining the BoardGameGeek." Significance 16, no. 5 (2019): 24–29. http://dx.doi.org/10.1111/j.1740-9713.2019.01317.x.

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19

Pravilovic, Sonja. "R Language in Data Mining Techniques and Statistics." American Journal of Software Engineering and Applications 2, no. 1 (2013): 7. http://dx.doi.org/10.11648/j.ajsea.20130201.12.

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20

Rayward-Smith, V. J. "Statistics to measure correlation for data mining applications." Computational Statistics & Data Analysis 51, no. 8 (2007): 3968–82. http://dx.doi.org/10.1016/j.csda.2006.05.025.

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21

Wegman, Edward J., Yasmin H. Said, and David W. Scott. "Machine learning, data mining, and computational statistics applications." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 3 (2011): 187. http://dx.doi.org/10.1002/wics.170.

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22

Grodzicka, Aneta. "Assessing Risky Behaviors Based on the Indicator Analysis of Statistics on Accidents at Work." Management Systems in Production Engineering 31, no. 4 (2023): 389–97. http://dx.doi.org/10.2478/mspe-2023-0043.

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Abstract The aim of the paper was to analyze risky behaviors in the mining industry on the example of the indicator analysis of the statistics on accidents at work. Selected criteria of generic analysis (structure index – causes of accidents) and indicator analysis (intensity index – frequency indicator of accidents) were used in the research. The source of data for the analysis of accident rate were the publications of the Statistics Poland entitled “Accidents at work” in Poland for 2010-2021. The study aimed at human causes, which included the following five groups: lack or improper handling

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23

Sanil, Ashish P. "Principles of Data Mining." Journal of the American Statistical Association 98, no. 461 (2003): 252–53. http://dx.doi.org/10.1198/jasa.2003.s257.

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24

Obenshain, Mary K. "Application of Data Mining Techniques to Healthcare Data." Infection Control & Hospital Epidemiology 25, no. 8 (2004): 690–95. http://dx.doi.org/10.1086/502460.

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AbstractA high-level introduction to data mining as it relates to surveillance of healthcare data is presented. Data mining is compared with traditional statistics, some advantages of automated data systems are identified, and some data mining strategies and algorithms are described. A concrete example illustrates steps involved in the data mining process, and three successful data mining applications in the healthcare arena are described.

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25

Onekpe, S. A., C. C. Nwachukwu, and E. A. Iyi. "Assessment of Mining Sector Contribution to Federal Government of Nigeria Revenue From 2012 to 2020." International Journal of Engineering and Environmental Sciences 6, no. 2 (2023): 40–49. https://doi.org/10.5281/zenodo.8307999.

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<em>This study aims to assess the contribution of the Mining Sector to the revenue of the Federal Government of Nigeria. The mining sector is a significant component of Nigeria's economy, rich in substantial mineral resources. The primary objective of this study is to evaluate the mining sector's contribution to the revenue of the Federal Government of Nigeria. The specific objectives are to identify the factors responsible for the decline in the mining sector’s revenue to the federal government and to make recommendations for enhancing the mining sector's contribution to the

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26

Sharma, Vikrant. "Improving Efficiency of High Utility Sequential Pattern Extraction." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 234–42. http://dx.doi.org/10.17762/msea.v70i1.2304.

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Text mining used on texts and publications in the biomedical and molecular biology fields is referred to as "biomedical text mining." It is a relatively new area of study at the intersection of computational linguistics, bioinformatics, and natural language processing. Superior usefulness the goal of sequential pattern mining is to identify statistically significant patterns among data instances when the values are presented sequentially. Time series mining is typically regarded as a distinct activity even if it is closely linked since it is typically assumed that the values are discrete. Stru

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27

Wu, Jinsui, Dongyu Xie, Sihai Yi, et al. "Fractal Study of the Development Law of Mining Cracks." Fractal and Fractional 7, no. 9 (2023): 696. http://dx.doi.org/10.3390/fractalfract7090696.

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Studying mining fracture development is vital for geotechnical and mining engineering and geological disaster prevention. This research assesses crack effects on rock mass stress equilibrium during coal mining, potentially causing geological disasters such as land subsidence and landslides. Using fractal geometry theory, the present study investigates the development of horizontal and vertical mining cracks, revealing their propagation patterns. The fractal dimension generally increases as the propulsion distance increases; however, fluctuations vary from 250 to 287.5 m, forming a wavering lin

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28

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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29

Wegman, Edward J. "Visual data mining." Statistics in Medicine 22, no. 9 (2003): 1383–97. http://dx.doi.org/10.1002/sim.1502.

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30

Patra, Rakesh. "Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 173–81. http://dx.doi.org/10.17762/msea.v70i1.2297.

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Data mining is the process of extracting new, possibly useful information from vast data bases that is not straightforward. Market basket analysis, a kind of data mining used in retail research, is used to analyse client transactions. The association between the things that occur in transactions more frequently was the focus of earlier data mining techniques. They don't take an item's significance or utility into account while often mining an itemset. Utility mining is a new field that has emerged as a result of the limits of common mining goods. When mining, the profitability or utility of an

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31

Sundararaman, Arun Thotapalli. "Exploring Insights through Visualization of Association Rules from Text Mining Statistics." Applied Mechanics and Materials 310 (February 2013): 567–71. http://dx.doi.org/10.4028/www.scientific.net/amm.310.567.

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Visualization is an important technique for analysis of knowledge derived from text mining. While different approaches exist for visualization, this paper presents a novel way of visualizing the strength of association between multiple terms that summarizes association in the form of a matrix. This approach is expected to improve the way decision makers analyze insights from text mining.

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32

Amin, Hetal, and Rohit Sharma. "How Data Mining is useful in Ayurveda." Journal of Ayurvedic and Herbal Medicine 2, no. 3 (2016): 61–62. http://dx.doi.org/10.31254/jahm.2016.2301.

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Data mining is a computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems. [1-3] The term data mining appeared around 1990 in the database community. Currently, data mining and knowledge discovery are used interchangeably.

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33

Gong, Peng Wei, and Li Hui Wang. "Development and Application of Mining Spares Management Information System." Advanced Materials Research 268-270 (July 2011): 1245–48. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1245.

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The mining spares management is an important part of mining enterprise information management. Starting from actual demands of mining enterprises and combining theories, this article develops mining spares management information system based on the BS framework. Major modules of the system includes functional modules such as basic data maintenance, spares inventory transaction processing, spares inventory transaction query and spares inventory information statistics and etc., so that the system basically realizes main functions of mining spares management and is able to give certain assistance

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34

Ahammer, H., J. M. Kröpfl, Ch Hackl, and R. Sedivy. "Image statistics and data mining of anal intraepithelial neoplasia." Pattern Recognition Letters 29, no. 16 (2008): 2189–96. http://dx.doi.org/10.1016/j.patrec.2008.08.008.

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35

Hartnett, Jessica L. "Using Data Mining to Teach Applied Statistics and Correlation." Teaching of Psychology 43, no. 2 (2016): 142–46. http://dx.doi.org/10.1177/0098628316636292.

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36

Jain, Kirti. "Clustering Algorithm of Data Mining to Detect Network Statistics." JIMS8I - International Journal of Information Communication and Computing Technology 5, no. 1 (2017): 268. http://dx.doi.org/10.5958/2347-7202.2017.00005.6.

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37

Sharma, Gaurav, Terje Haukaas, Robert A. Hall, and Suraj Priyadarshini. "Bayesian statistics and production reliability assessments for mining operations." International Journal of Mining, Reclamation and Environment 23, no. 3 (2009): 180–205. http://dx.doi.org/10.1080/17480930902916262.

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38

Foster, Dean P., and Robert A. Stine. "Variable Selection in Data Mining." Journal of the American Statistical Association 99, no. 466 (2004): 303–13. http://dx.doi.org/10.1198/016214504000000287.

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39

Mahmoudi, Mohammad Reza, and Ali Abbasalizadeh. "How statistics and text mining can be applied to literary studies?" Digital Scholarship in the Humanities 34, no. 3 (2018): 536–41. http://dx.doi.org/10.1093/llc/fqy069.

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Abstract Statistics and data mining techniques provide exciting approaches for extracting knowledge from data. Recently, using statistics and data mining has sought to be exploited in many research fields. In this study, it was demonstrated that how statistics can be applied to literary studies. First, all the lines in Khaghani’s divan are classified and coded into three categories (mystical, non-mystical, and borderline). Then a set of chi-square goodness-of-fit tests are used to investigate and compare the frequency of different line’s categories for all lines and all odes, separately. Final

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40

Zhu, Jian Xin. "Arithmetic Research on Data Mining Technology and Associative Rules Mining." Applied Mechanics and Materials 556-562 (May 2014): 3949–51. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3949.

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Data mining is a technique that aims to analyze and understand large source data reveal knowledge hidden in the data. It has been viewed as an important evolution in information processing. Why there have been more attentions to it from researchers or businessmen is due to the wide availability of huge amounts of data and imminent needs for turning such data into valuable information. During the past decade or over, the concepts and techniques on data mining have been presented, and some of them have been discussed in higher levels for the last few years. Data mining involves an integration of

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41

Yang, Bin. "Research on Customer Value Evaluation Based on Data Mining and Mathematical Statistics." Advanced Materials Research 926-930 (May 2014): 3890–93. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3890.

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In business you can get a number of data about customer information. How to find useful information for business decision-making from so many complicated, messy data and then to perform customer value assessment is a very important and complicated process. In this paper, data mining topics is identified as customer value assessment to assess customer value through data mining and statistical methods, in order to support the company's marketing decision making and customer relationship management decision making.

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42

Sharma, Dr Nirmla, and Sameera Iqbal Muhmmad Iqbal. "Emerging Need for Disruption in the Next Trend of Artificial Intelligence-Controlled Transformation Using Knowledge Mining." International Journal of Engineering and Advanced Technology 14, no. 3 (2025): 26–32. https://doi.org/10.35940/ijeat.c4564.14030225.

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Knowledge mining is an emerging type of artificial intelligence (AI), that uses a grouping of AI facilities to determine satisfied thought over huge volumes of unstructured, semi-structured, and structured data that permit industries to extremely recognize their data, search it, expose visions and found associations and designs at scale. Although the initial trend of AI contained numerous slight applications, such as the preparation of a particular model over a single statistics basis of a positive kind for a particular problem, knowledge mining is the next trend of Artificial Intelligence, pr

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43

Dr., Nirmla Sharma. "Emerging Need for Disruption in the Next Trend of Artificial Intelligence-Controlled Transformation Using Knowledge Mining." International Journal of Engineering and Advanced Technology (IJEAT) 14, no. 3 (2025): 26–32. https://doi.org/10.35940/ijeat.C4564.14030225.

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<strong>Abstract: </strong>Knowledge mining is an emerging type of artificial intelligence (AI), that uses a grouping of AI facilities to determine satisfied thought over huge volumes of unstructured, semi-structured, and structured data that permit industries to extremely recognize their data, search it, expose visions and found associations and designs at scale. Although the initial trend of AI contained numerous slight applications, such as the preparation of a particular model over a single statistics basis of a positive kind for a particular problem, knowledge mining is the next trend of

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44

Zhang, Dan, and Hua Wang. "The Ecological Risk Assessment of Forest Damage in ”A" Coal Mining Area." Advanced Materials Research 726-731 (August 2013): 1183–89. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.1183.

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This paper, through the layer-by-layer decomposition of risk sources and risk receptor, constructs an ecological risk assessment indicator system of damaged forest in coal mining area. Based on the reality of A coal mining area, with the application of improved risk model, this paper processes and analyzes the statistics and solid - state survey data in A coal mining area to get the ecological effects of forest damage led by coal mining in that area, and calculates the loss value, the compensation amount and the compensation scale of the forest ecosystem service function in the subsidence area

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45

Marras, Elisabetta, and Enrico Capobianco. "Mining protein–protein interaction networks: denoising effects." Journal of Statistical Mechanics: Theory and Experiment 2009, no. 01 (2009): P01006. http://dx.doi.org/10.1088/1742-5468/2009/01/p01006.

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46

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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47

Last, M. "Pattern Recognition Algorithms for Data Mining." Journal of the American Statistical Association 102, no. 478 (2007): 759. http://dx.doi.org/10.1198/jasa.2007.s186.

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48

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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49

Fortmeyer, Sophia. "“Why Can’t I Find It?”." Undergraduate Research Journal for the Humanities 3, no. 1 (2018): 49–66. http://dx.doi.org/10.17161/1808.26396.

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This research focuses on foreign intervention in the mining sectors of Zambia and the Democratic Republic of Congo (DRC), concentrating on the transparency of agreements and regulation, or lack thereof, contributing to the longevity of mining practices and the livelihoods of local citizens. The current state of public information regarding natural resource extraction in both countries creates questions about state motives and investors’ economic incentive, with consequences of inequality, human rights offenses, and underdevelopment. Applying Immanuel Wallerstein’s world-systems theory, I descr

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50

Weiß, Christian H. "Statistical mining of interesting association rules." Statistics and Computing 18, no. 2 (2007): 185–94. http://dx.doi.org/10.1007/s11222-007-9047-6.

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