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Journal articles on the topic 'Data mining. Computer Science'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Musicant, David R. "A data mining course for computer science." ACM SIGCSE Bulletin 38, no. 1 (2006): 538–42. http://dx.doi.org/10.1145/1124706.1121508.

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2

Malkić, Jasmin, and Nermin Sarajlić. "INTERDISCIPLINARY APPLICATION OF ALGORITHMS FOR DATA MINING." Journal Human Research in Rehabilitation 3, no. 2 (2013): 6–9. http://dx.doi.org/10.21554/hrr.091303.

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Interdisciplinary application of data mining is linked with the ability to receive and process the large amounts of data. Although even the first computers could help in executing the tasks that required accuracy and reliability atypical to the human way of information processing, only increasing the speed of computer processors and advances in computer science have introduced the possibility that computers can play a more active role in decision making. Applications of these features are found in medicine, where data mining is used in clinical trials to determine the factors that influence he

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3

Radhakrishna, Vangipuram, Gunupudi Rajesh Kumar, Gali Suresh Reddy, and Dammavalam Srinivasa Rao. "Machine Learning for Data Mining, Data Science and Data Analytics." Recent Advances in Computer Science and Communications 14, no. 5 (2021): 1356–57. http://dx.doi.org/10.2174/266625581405210129161546.

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4

Bin, Liu, Zhang Hui, Liu Sifeng, and Dang Yaoguo. "Data mining techniques based on grey system theories for time sequence data." Computer Science and Information Systems 3, no. 2 (2006): 73–82. http://dx.doi.org/10.2298/csis0602073b.

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Data mining is an interesting focus in computer science field now This paper deals with data mining techniques based on Grey system theories for time sequence data. Firstly, thoughts of data mining with embedded knowledge are expatiated, and the status quo of Data mining techniques is presented briefly. Then, based on the above thoughts and the Grey system theories, data mining techniques based on Grey system theories for time sequence data are proposed for the first time, and the idiographic arithmetic with GM(1,1) as an example is introduced in this paper. Last, it forecasts the total homes

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5

Wu, Xiang Min, Cheng Lin Zhao, and Pan Cao. "Research of Data Base and Data Mining in CRM." Applied Mechanics and Materials 543-547 (March 2014): 2988–91. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2988.

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Customer Relationship Management (CRM) is becoming the focus of enterprise and an active research field of computer science. The ariticle introduces some basic concepts about CRM and data mining, and some benefits brought by data mining in CRM. At the end it points out how to apply data mining applications in CRM.

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6

Velásquez-Pérez, T., J. A. Camargo-Pérez, and E. L. Quintero-Quintero. "Application of data mining as a tool in computer science." Journal of Physics: Conference Series 1587 (July 2020): 012018. http://dx.doi.org/10.1088/1742-6596/1587/1/012018.

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7

McCarthy, John. "Phenomenal data mining." Communications of the ACM 43, no. 8 (2000): 75–79. http://dx.doi.org/10.1145/345124.345152.

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8

Mikut, Ralf, and Markus Reischl. "Data mining tools." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, no. 5 (2011): 431–43. http://dx.doi.org/10.1002/widm.24.

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9

Leung, Carson Kai-Sang. "Mining uncertain data." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, no. 4 (2011): 316–29. http://dx.doi.org/10.1002/widm.31.

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10

Fayyad, Usama, David Haussler, and Paul Stolorz. "Mining scientific data." Communications of the ACM 39, no. 11 (1996): 51–57. http://dx.doi.org/10.1145/240455.240471.

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11

Li, Xingsen, Yong Shi, Jun Li, and Peng Zhang. "Data Mining Consulting Improve Data Quality." Data Science Journal 6 (2007): S658—S666. http://dx.doi.org/10.2481/dsj.6.s658.

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12

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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13

Coenen, Frans. "Data mining: past, present and future." Knowledge Engineering Review 26, no. 1 (2011): 25–29. http://dx.doi.org/10.1017/s0269888910000378.

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AbstractData mining has become a well-established discipline within the domain of artificial intelligence (AI) and knowledge engineering (KE). It has its roots in machine learning and statistics, but encompasses other areas of computer science. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale data mining to be conducted. Unlike other innovations in AI and KE, data mining can be argued to be an application rather then a technology and thus can be expected to remain topical for the foreseeable future. Thi

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14

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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15

Shrestha, Sushil, and Manish Pokharel. "Educational data mining in moodle data." International Journal of Informatics and Communication Technology (IJ-ICT) 10, no. 1 (2021): 9. http://dx.doi.org/10.11591/ijict.v10i1.pp9-18.

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<p>The main purpose of this research paper is to analyze the moodle data and identify the most influencing features to develop the predictive model. The research applies a wrapper-based feature selection method called Boruta for the selection of best predicting features. Data were collected from eighty-one students who were enrolled in the course called Human Computer Interaction (COMP341), offered by the Department of Computer Science and Engineering at Kathmandu University, Nepal. Kathmandu University uses Moodle as an e-learning platform. The dataset contained eight features where Ass

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16

Inmon, W. H. "The data warehouse and data mining." Communications of the ACM 39, no. 11 (1996): 49–50. http://dx.doi.org/10.1145/240455.240470.

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17

Da San Martino, Giovanni, and Alessandro Sperduti. "Mining Structured Data." IEEE Computational Intelligence Magazine 5, no. 1 (2010): 42–49. http://dx.doi.org/10.1109/mci.2009.935308.

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18

Zheng, Yu. "Trajectory Data Mining." ACM Transactions on Intelligent Systems and Technology 6, no. 3 (2015): 1–41. http://dx.doi.org/10.1145/2743025.

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19

Hirji, Karim K. "Exploring data mining implementation." Communications of the ACM 44, no. 7 (2001): 87–93. http://dx.doi.org/10.1145/379300.379323.

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20

Grossman, Robert L., Mark F. Hornick, and Gregor Meyer. "Data mining standards initiatives." Communications of the ACM 45, no. 8 (2002): 59–61. http://dx.doi.org/10.1145/545151.545180.

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21

Zhang, Zhiyong, and Danyang Zhang. "What is Data Science? An Operational Definition based on Text Mining of Data Science Curricula." Journal of Behavioral Data Science 1, no. 1 (2021): 1–16. http://dx.doi.org/10.35566/jbds/v1n1/p1.

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Data science has maintained its popularity for about 20 years. This study adopts a bottom-up approach to understand what data science is by analyzing the descriptions of courses offered by the data science programs in the United States. Through topic modeling, 14 topics are identified from the current curricula of 56 data science programs. These topics reiterate that data science is at the intersection of statistics, computer science, and substantive fields.

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22

Washio, Takashi. "Special Issue on Data-Mining and Statistical Science." New Generation Computing 27, no. 4 (2009): 281–84. http://dx.doi.org/10.1007/s00354-009-0065-0.

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23

Brito, Paula, and Donato Malerba. "Mining official data." Intelligent Data Analysis 7, no. 6 (2003): 497–500. http://dx.doi.org/10.3233/ida-2003-7601.

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24

Yang, Qiang. "Three challenges in data mining." Frontiers of Computer Science in China 4, no. 3 (2010): 324–33. http://dx.doi.org/10.1007/s11704-010-0102-7.

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25

Sun, Xiang Kun. "The Development and Research of Data Mining Technology." Applied Mechanics and Materials 602-605 (August 2014): 3461–64. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3461.

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With the development of computer science and database system, millions of data will be generated every day. How to mining useful information and knowledge from large database is becoming a more and more popular research topic. In this paper, we introduced the origin of data mining, discussed some different data mining techniques in different fields, and made a conclusion of the application of data mining.

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26

Smyth, Padhraic, Daryl Pregibon, and Christos Faloutsos. "Data-driven evolution of data mining algorithms." Communications of the ACM 45, no. 8 (2002): 33–37. http://dx.doi.org/10.1145/545151.545175.

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27

Abe, Akinori, Norihiro Hagita, Michiko Furutani, Yoshiyuki Furutani, and Rumiko Matsuoka. "Possibility of Integrated Data Mining of Clinical Data." Data Science Journal 6 (2007): S104—S115. http://dx.doi.org/10.2481/dsj.6.s104.

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28

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 (2009): 1018–27. http://dx.doi.org/10.1007/s11390-009-9291-7.

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29

Mitchell, Tom M. "Machine learning and data mining." Communications of the ACM 42, no. 11 (1999): 30–36. http://dx.doi.org/10.1145/319382.319388.

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30

Zhang, Shichao. "Information enhancement for data mining." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, no. 4 (2011): 284–95. http://dx.doi.org/10.1002/widm.21.

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31

Gaber, Mohamed Medhat. "Advances in data stream mining." WIREs Data Mining and Knowledge Discovery 2, no. 1 (2011): 79–85. http://dx.doi.org/10.1002/widm.52.

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32

Glymour, Clark, David Madigan, Daryl Pregibon, and Padhraic Smyth. "Statistical inference and data mining." Communications of the ACM 39, no. 11 (1996): 35–41. http://dx.doi.org/10.1145/240455.240466.

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33

Apte, Chidanand, Bing Liu, Edwin P. D. Pednault, and Padhraic Smyth. "Business applications of data mining." Communications of the ACM 45, no. 8 (2002): 49–53. http://dx.doi.org/10.1145/545151.545178.

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34

Berzal, Fernando, Ignacio Blanco, Juan-Carlos Cubero, and Nicolas Marin. "Component-based data mining frameworks." Communications of the ACM 45, no. 12 (2002): 97–100. http://dx.doi.org/10.1145/585597.585624.

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35

Hoffmann, Leah. "Data mining meets city hall." Communications of the ACM 55, no. 6 (2012): 19–21. http://dx.doi.org/10.1145/2184319.2184326.

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36

Kogan, J., C. Nicholas, and V. Volkovich. "Data Mining - Text mining with information-theoretic clustering." Computing in Science & Engineering 5, no. 6 (2003): 52–59. http://dx.doi.org/10.1109/mcise.2003.1238704.

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37

Domingo-Ferrer, Josep, and Vicenç Torra. "Privacy in Data Mining." Data Mining and Knowledge Discovery 11, no. 2 (2005): 117–19. http://dx.doi.org/10.1007/s10618-005-0009-3.

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38

Chen, Tung-Shou, Jeanne Chen, and Yuan-Hung Kao. "A Novel Hybrid Protection Technique of Privacy-Preserving Data Mining and Anti-Data Mining." Information Technology Journal 9, no. 3 (2010): 500–505. http://dx.doi.org/10.3923/itj.2010.500.505.

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39

Ghosh, Joydeep. "Data Mining Technical Committee." IEEE Computational Intelligence Magazine 2, no. 2 (2007): 74. http://dx.doi.org/10.1109/mci.2007.353425.

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40

Buck, Christian, Tobias Gass, Andreas Hannig, et al. "Data-Mining-Cup 2007." Informatik-Spektrum 31, no. 6 (2008): 591–99. http://dx.doi.org/10.1007/s00287-008-0239-z.

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41

Grosskreutz, Henrik, Benedikt Lemmen, and Stefan Rüping. "Privacy-Preserving Data-Mining." Informatik-Spektrum 33, no. 4 (2010): 380–83. http://dx.doi.org/10.1007/s00287-010-0447-1.

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42

Qiu, Yongxiao, Guanghui Du, and Song Chai. "A Novel Algorithm for Distributed Data Stream Using Big Data Classification Model." International Journal of Information Technology and Web Engineering 15, no. 4 (2020): 1–17. http://dx.doi.org/10.4018/ijitwe.2020100101.

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In order to solve the problem of real-time detection of power grid equipment anomalies, this paper proposes a data flow classification model based on distributed processing. In order to realize distributed processing of power grid data flow, a local node mining method and a global mining mode based on uneven data flow classification are designed. A data stream classification model based on distributed processing is constructed, then the corresponding data sequence is selected and formatted abstractly, and the local node mining method and global mining mode under this model are designed. In the

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43

Matshonisa Seeletse, Solly, Tsakani Violet Ndobe, Tichavasia Alex Dandadzi, and Taurai Hungwe. "Crowdsourcing benefits in postgraduate project supervision: Sefako Makgatho Health Sciences University statistics and computer science case study." Environmental Economics 7, no. 2 (2016): 122–29. http://dx.doi.org/10.21511/ee.07(2).2016.13.

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The paper reports on the findings made on an experiential exercise of Bachelor of Science Honours in Statistics (BSc Hons Stat) in the Department of Statistics and Operations Research (SOR) of the Sefako Makgatho Health Sciences University (SMU) in South Africa. SOR is a small, understaffed department, which offers courses for degrees from Bachelor to Doctoral levels in the subfields of Artificial Intelligence, Data Mining, Operations Research, Statistics and related ones. On SMU campus, expertize in some of these fields is also available in the Department of Computer Science (DCS). In the 201

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44

Yin, Yunfei, Lianjie Long, and Xiyu Deng. "Dynamic Data Mining of Sensor Data." IEEE Access 8 (2020): 41637–48. http://dx.doi.org/10.1109/access.2020.2976699.

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45

Poovammal, E., and M. Ponnavaikko. "Utility Independent Privacy Preserving Data Mining - Horizontally Partitioned Data." Data Science Journal 9 (2010): 62–72. http://dx.doi.org/10.2481/dsj.008-040.

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46

Giraud-Carrier, C., and O. Povel. "Characterising Data Mining software." Intelligent Data Analysis 7, no. 3 (2003): 181–92. http://dx.doi.org/10.3233/ida-2003-7302.

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47

Abe, Hidenao. "Data Mining Tool:Weka." Journal of The Institute of Image Information and Television Engineers 65, no. 10 (2011): 1398–401. http://dx.doi.org/10.3169/itej.65.1398.

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48

Shaheen, Muhammad, Ali Ahsan, and Saeed Iqbal. "Data Mining of Scientometrics for Classifying Science Journals." Intelligent Automation & Soft Computing 28, no. 3 (2021): 873–85. http://dx.doi.org/10.32604/iasc.2021.016622.

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49

Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding. "Data mining with big data." IEEE Transactions on Knowledge and Data Engineering 26, no. 1 (2014): 97–107. http://dx.doi.org/10.1109/tkde.2013.109.

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50

?alik, Krista Rizman. "Learning through data mining." Computer Applications in Engineering Education 13, no. 1 (2005): 60–65. http://dx.doi.org/10.1002/cae.20030.

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