广东工业大学学报 ›› 2014, Vol. 31 ›› Issue (1): 25-31.doi: 10.3969/j.issn.1007-7162.2014.01.005

• 综合研究 • 上一篇    下一篇

基于KNN的多组合器协同挖掘局域气象数据

滕少华1,樊继慧1,陈潇2,张巍1,刘冬宁1,梁路1   

  1. 1.广东工业大学 计算机学院,广东 广州 510060;2.广州市萝岗区气象局,广东 广州 510530
  • 收稿日期:2013-11-25 出版日期:2014-03-29 发布日期:2014-03-29
  • 作者简介:滕少华(1962-),男,教授,博士,主要研究方向为协同计算、数据挖掘、网络安全、大数据、复杂网络下的协同建模与绿色计算.
  • 基金资助:

    教育部重点实验室基金资助项目(110411);广东省自然科学基金资助项目(10451009001004804,9151009001000007);广东省科技计划项目(2012B091000173);广州市科技计划项目(2012J5100054,2013J4500028);韶关市科技计划资助项目(2010CXY/C05)

The Application of KNNbased Multiclassifiers in Mining Local Area Meteorological Data

Teng Shao-hua1, Fan Ji-hui1, Chen Xiao2, Zhang Wei1, Liu Dong-ning1, Liang Lu1   

  1. 1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China;2.Luogang District Bureau of Meteorology, Guangzhou 510530, China
  • Received:2013-11-25 Online:2014-03-29 Published:2014-03-29

摘要: 自然灾害已严重威胁人们的生命财产安全,影响天气变化的因素多而复杂,致使灾害天气的准确预测预报相当困难.本文从局部区域出发,应用多组合器协同分析方法,探讨局域环境下的气象数据挖掘问题,提出了一个多组合器协同分析模型,实现了各基分类器和组合器的建模,通过对气象数据的实证性分析与实验,验证了本模型有较高的分类准确率和快速分类能力.

关键词: 数据挖掘;K-最近邻算法;协同;组合分析器;局域气象数据

Abstract: Natural disasters injure people and damage property. Because the weather is related to many factors, it is difficult to forecast accurately the disastrous weather. Based on multiclassifiers, a cooperative data mining method is proposed. A cooperative classification model is designed and implemented, which is composed of base classifiers and an integration classifier. The model is used to mine local area meteorological data. Experimental results show that the model has high classification accuracy and efficient ability.

Key words: data mining; K-Nearest Neighbor algorithm; cooperative; multiclassifier model; local area meteorological data

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