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一种新的聚类判别分析框架及其实证研究
  • ISSN号：1001-3695
  • 期刊名称：《计算机应用研究》
  • 时间：0
  • 分类：TP301.6[自动化与计算机技术—计算机系统结构;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]四川大学工商管理学院,成都610064
  • 相关基金：国家杰出青年科学基金资助项目（70425005）; 教育部高等学校优秀青年教师教学科研奖励计划资助项目（20023834-3）
作者： 何跃[1], 杨磊[1], 徐玖平[1]
关键词： 聚类分析, 判别分析, 动态聚类, clustering analysis, discriminating analysis, dynamic clustering analysis
中文摘要：

在分析经典聚类判别分析方法实质的基础上,提出了一种新的聚类判别分析框架,改进了一种基于样本指标值频度计算的两总体判别分析算法,提高了在对所有参与建立判别模型的样本进行判别时的计算速度;给出了建立在此改进判别分析算法基础上的一种动态聚类判别分析算法的设计,并实现了所有算法。进行相应的实证研究,结果表明以此聚类判别分析框架对给定样本集合进行分析,可以迅速得到多个合理的聚类结果以及对聚类结果的清晰解释,既可以对已有的聚类结果进行验证,又可以进行数据的探索性分析。

英文摘要：

This paper gave the essential of classical clustering and discriminating analysis. Improved the efficiency of a two- group discriminating analysis method. Then gave the design of a dynamic clustering analysis method based on this discriminating analysis method, and related empirical studies were also done. The outcome shows that this new class of analysis frame can get more reasonable clustering results with clear explanations, and it not only can validate the known clustering results but also can be used for explorative analysis.

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期刊信息
  • 《计算机应用研究》
  • 北大核心期刊（2011版）
  • 主管单位:四川省科学技术厅
  • 主办单位:四川省计算机研究院
  • 主编：刘营
  • 地址：成都市成科西路3号
  • 邮编：610041
  • 邮箱：arocmag@163.com
  • 电话：028-85210177 85249567
  • 国际标准刊号：ISSN：1001-3695
  • 国内统一刊号：ISSN：51-1196/TP
  • 邮发代号:62-68
  • 获奖情况:
  • 第二届国家期刊奖百种重点科技期刊,国内计算技术类重点核心期刊,国内外著名数据库收录期刊
  • 国内外数据库收录:
  • 俄罗斯文摘杂志,波兰哥白尼索引,英国科学文摘数据库,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,中国北大核心期刊（2000版）
  • 被引量:60049