Research on Data Mining Technology Based on Weka Platform

Wang Pan Zao

doi:doi:10.11648/j.sd.20170504.18

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Research on Data Mining Technology Based on Weka Platform

Wang Pan Zao

Published in Science Discovery (Volume 5, Issue 4)

Received: 6 June 2017 Published: 8 June 2017

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Abstract

Research uses Weka big data mining technology platform to analyze the data.The association rules miningmethods of discrete sample data by Weka technology,using SimpleKMeans clustering algorithm for clustering analysisof thesimulated sample data mining, common features of each type of data and the difference data between differentclusters from where, for a variety of data region division, analysis of different regions of the data distribution. For example, mining research project, a school in the college entrance examination scores data for the simulation sample, in Chinese, math and English college entrance examination scores as the object of analysis of large data mining, thepaper in science classes, the language, the total scores were compared with the distribution.The integrated use of statistical analysis and data mining technology, mining analysis on college entrance examination data deeply, get useful informationwith performance clustering, has strong theoreticalvalue, can help to the college entrance examination reform, give some guidance to high school education.

Published in	Science Discovery (Volume 5, Issue 4)
DOI	10.11648/j.sd.20170504.18
Page(s)	287-292
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Data Mining, Cluster Analysis, Weka Platform, College Entrance Examination

References

[1]	胡志伟．关于大数据治理研究与分析 [J]．物联网世界，2014（08）：58。
[2]	纪希禹．数据挖掘技术实例 [M]．机械工业出版社，北京：机械工业出版社，2009:102。
[3]	陶雪娇，胡晓峰，刘洋．大数据研究综述 [J]．系统仿真学报，2013(01):142-146。
[4]	张引，陈敏，廖小飞．大数据应用的现状与展望 [J]．计算机研究与发展，2013(02):216-233。
[5]	邬贺铨．大数据思维 [J]．科学与社会，2014(01)：76-77。
[6]	JeffreyDean, Sanjay, Ghemawat.MapReduce:simplified data processing on large cluster [J]. Communications of the ACM, 2013, 51(1):107–113.
[7]	Daniel J.Power.UsingBig Data for analytics and decision support [J]. Journal of Decision Systems, 2014(2): 78。
[8]	Shi XiaLiu,Michelle Xzhou, ShimeiPan. TIARA [J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2012 (2):108。
[9]	程学旗，靳小龙，王元卓．大数据系统和分析技术综述 [J]．软件学报，2014(09)：123-124。
[10]	耿直．大数据时代统计学面临的机遇与挑战 [J]．统计研究，2014(02):89。
[11]	李金昌．大数据与统计新思维 [J]．统计研究，2014(01):167。
[12]	姚徐．数据挖掘在计算机等级考试中的应用 [J]．计算机光盘软件与应用，2013（01）：55。
[13]	柳玉巧．聚类分析和关联规则技术在成绩分析中的研究及应用 [D]．武汉：华中师范大学，2014。

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Wang Pan Zao. (2017). Research on Data Mining Technology Based on Weka Platform. Science Discovery, 5(4), 287-292. https://doi.org/10.11648/j.sd.20170504.18

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Wang Pan Zao. Research on Data Mining Technology Based on Weka Platform. Sci. Discov. 2017, 5(4), 287-292. doi: 10.11648/j.sd.20170504.18

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Wang Pan Zao. Research on Data Mining Technology Based on Weka Platform. Sci Discov. 2017;5(4):287-292. doi: 10.11648/j.sd.20170504.18

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@article{10.11648/j.sd.20170504.18,
  author = {Wang Pan Zao},
  title = {Research on Data Mining Technology Based on Weka Platform},
  journal = {Science Discovery},
  volume = {5},
  number = {4},
  pages = {287-292},
  doi = {10.11648/j.sd.20170504.18},
  url = {https://doi.org/10.11648/j.sd.20170504.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20170504.18},
  abstract = {Research uses Weka big data mining technology platform to analyze the data.The association rules miningmethods of discrete sample data by Weka technology,using SimpleKMeans clustering algorithm for clustering analysisof thesimulated sample data mining, common features of each type of data and the difference data between differentclusters from where, for a variety of data region division, analysis of different regions of the data distribution. For example, mining research project, a school in the college entrance examination scores data for the simulation sample, in Chinese, math and English college entrance examination scores as the object of analysis of large data mining, thepaper in science classes, the language, the total scores were compared with the distribution.The integrated use of statistical analysis and data mining technology, mining analysis on college entrance examination data deeply, get useful informationwith performance clustering, has strong theoreticalvalue, can help to the college entrance examination reform, give some guidance to high school education.},
 year = {2017}
}

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TY  - JOUR
T1  - Research on Data Mining Technology Based on Weka Platform
AU  - Wang Pan Zao
Y1  - 2017/06/08
PY  - 2017
N1  - https://doi.org/10.11648/j.sd.20170504.18
DO  - 10.11648/j.sd.20170504.18
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 287
EP  - 292
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20170504.18
AB  - Research uses Weka big data mining technology platform to analyze the data.The association rules miningmethods of discrete sample data by Weka technology,using SimpleKMeans clustering algorithm for clustering analysisof thesimulated sample data mining, common features of each type of data and the difference data between differentclusters from where, for a variety of data region division, analysis of different regions of the data distribution. For example, mining research project, a school in the college entrance examination scores data for the simulation sample, in Chinese, math and English college entrance examination scores as the object of analysis of large data mining, thepaper in science classes, the language, the total scores were compared with the distribution.The integrated use of statistical analysis and data mining technology, mining analysis on college entrance examination data deeply, get useful informationwith performance clustering, has strong theoreticalvalue, can help to the college entrance examination reform, give some guidance to high school education.
VL  - 5
IS  - 4
ER  -

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Author Information

Wang Pan Zao

Department of Information and Engineering, Sichuan Tourism University, Chengdu, China

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Wang Pan Zao. (2017). Research on Data Mining Technology Based on Weka Platform. Science Discovery, 5(4), 287-292. https://doi.org/10.11648/j.sd.20170504.18

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ACS Style

Wang Pan Zao. Research on Data Mining Technology Based on Weka Platform. Sci. Discov. 2017, 5(4), 287-292. doi: 10.11648/j.sd.20170504.18

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AMA Style

Wang Pan Zao. Research on Data Mining Technology Based on Weka Platform. Sci Discov. 2017;5(4):287-292. doi: 10.11648/j.sd.20170504.18

Copy | Download

@article{10.11648/j.sd.20170504.18,
  author = {Wang Pan Zao},
  title = {Research on Data Mining Technology Based on Weka Platform},
  journal = {Science Discovery},
  volume = {5},
  number = {4},
  pages = {287-292},
  doi = {10.11648/j.sd.20170504.18},
  url = {https://doi.org/10.11648/j.sd.20170504.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20170504.18},
  abstract = {Research uses Weka big data mining technology platform to analyze the data.The association rules miningmethods of discrete sample data by Weka technology,using SimpleKMeans clustering algorithm for clustering analysisof thesimulated sample data mining, common features of each type of data and the difference data between differentclusters from where, for a variety of data region division, analysis of different regions of the data distribution. For example, mining research project, a school in the college entrance examination scores data for the simulation sample, in Chinese, math and English college entrance examination scores as the object of analysis of large data mining, thepaper in science classes, the language, the total scores were compared with the distribution.The integrated use of statistical analysis and data mining technology, mining analysis on college entrance examination data deeply, get useful informationwith performance clustering, has strong theoreticalvalue, can help to the college entrance examination reform, give some guidance to high school education.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Research on Data Mining Technology Based on Weka Platform
AU  - Wang Pan Zao
Y1  - 2017/06/08
PY  - 2017
N1  - https://doi.org/10.11648/j.sd.20170504.18
DO  - 10.11648/j.sd.20170504.18
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 287
EP  - 292
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20170504.18
AB  - Research uses Weka big data mining technology platform to analyze the data.The association rules miningmethods of discrete sample data by Weka technology,using SimpleKMeans clustering algorithm for clustering analysisof thesimulated sample data mining, common features of each type of data and the difference data between differentclusters from where, for a variety of data region division, analysis of different regions of the data distribution. For example, mining research project, a school in the college entrance examination scores data for the simulation sample, in Chinese, math and English college entrance examination scores as the object of analysis of large data mining, thepaper in science classes, the language, the total scores were compared with the distribution.The integrated use of statistical analysis and data mining technology, mining analysis on college entrance examination data deeply, get useful informationwith performance clustering, has strong theoreticalvalue, can help to the college entrance examination reform, give some guidance to high school education.
VL  - 5
IS  - 4
ER  -

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