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大数据时代的本科数据挖掘课程建设 被引量:2

Data mining course construction in the era of big data
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摘要 数据挖掘技术是大数据时代的关键技术和核心内容。本科数据挖掘课程系统地介绍数据挖掘的基本概念、基本原理和应用技术,以及大数据背景下数据挖掘的特点及新技术。针对本科生的特点,课程尽量弱化理论和算法,强调应用。通过对各种实例的分析和实验,使学生面对具体应用问题时,能够利用SPSS Modeler设计数据处理的过程,选取合适的数据挖掘方法,并最终得到较理想的数据挖掘结果。 Data mining technology is the key technology and core content of the era of big data. The basic concept, basic principle and application technology of data mining are introduced in this undergraduate course, and the characteristics and new technology of data mining in the background of big data are also introduced. Aiming at the characteristics of undergraduate, the course tries to weaken the theory and algorithm, emphasizing the application. Through analysis and experiments on a variety of examples, when faced with a specific application, students can use SPSS modeler to design data processing process, select the appropriate data mining methods, and eventually get ideal results in data mining.
出处 《计算机时代》 2016年第9期76-79,共4页 Computer Era
基金 2015教育部人文社科基金项目"校企深度合作大数据人才培养模式研究和实践"(15JDGC015)
关键词 大数据 数据挖掘 本科课程 SPSS MODELER big data data mining undergraduate course SPSS modeler
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  • 1梅立军,周强,臧路,陈祖舜.知网与同义词词林的信息融合研究[J].中文信息学报,2005,19(1):63-70. 被引量:28
  • 2易超琴,万建平.我国电信收入的统计分析[J].统计与决策,2005,21(09X):114-115. 被引量:4
  • 3董振东,董强,郝长伶.知网的理论发现[J].中文信息学报,2007,21(4):3-9. 被引量:97
  • 4Kumar U,Jain V K. Time series models (grey-Markov, grey model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India [ J ]. Energy, 2010,35 (4) :1709-1716.
  • 5Kayacan E, Ulutas B, Kaynak O. Grey system theory- based models in time series prediction[J]. Expert Systems with Ap- plications ,2010,37 ( 3 ) : 1784-1789.
  • 6Boubaker A, Makram B. Modeling heavy tails and double long memory in North African stock market returns[ ] ]. North Afri-can Studies, 2012, 2 (3) :195-214.
  • 7Claeskens G, Hjort N L. Model Selection and Model Averaging [ M ]. Cambridge: Cambridge University Press, 2008.
  • 8Baheer I A, Hajmeer M. Artificial neural networks : fundamen- tals, computing, design and application[ J 1.Microbiological Methods,2000,43 ( 1 ) :3-31.
  • 9CRISP-DM1.0数据挖掘方法论指南[M].出版地不祥:CRISP-DM协会,2000.
  • 10Chris Anderson. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired, 2008, 16 (7).

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