[1]朱 虹,李千目,戚湧.一种基于改进最近邻算法的忠诚度预测方法[J].南京理工大学学报(自然科学版),2017,41(04):448.[doi:10.14177/j.cnki.32-1397n.2017.41.04.008]
 Zhu Hong,Li Qianmu,Qi Yong.Loyalty prediction method based on improvednearest neighbor algorithm[J].Journal of Nanjing University of Science and Technology,2017,41(04):448.[doi:10.14177/j.cnki.32-1397n.2017.41.04.008]
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一种基于改进最近邻算法的忠诚度预测方法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
41卷
期数:
2017年04期
页码:
448
栏目:
出版日期:
2017-08-31

文章信息/Info

Title:
Loyalty prediction method based on improvednearest neighbor algorithm
文章编号:
1005-9830(2017)04-0448-06
作者:
朱 虹李千目戚湧
南京理工大学 计算机科学与工程学院,江苏 南京 210094
Author(s):
Zhu HongLi QianmuQi Yong
School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
关键词:
数据挖掘 分类 聚类 回归 K最近邻算法 贝叶斯算法 忠诚度预测
Keywords:
data mining classification clustering regression nearest neighbor algorithm bayesian algorithm loyalty prediction
分类号:
TP391
DOI:
10.14177/j.cnki.32-1397n.2017.41.04.008
摘要:
为了提高忠诚度预测的准确度和效率,该文将K最近邻(K nearest neighbor,KNN)算法和贝叶斯算法相结合作为分类预测策略,提出一种基于改进最近邻算法的忠诚度预测方法。该方法先将高忠诚客户和低忠诚客户当作同一忠诚度类别,即同属于忠诚客户类别,利用贝叶斯算法对数据集进行初步分类,获得非忠诚客户和忠诚客户,再将忠诚客户作为下一步KNN算法的测试数据,对其做进一步分类,得到高忠诚客户、低忠诚客户和非忠诚客户。实验结果表明,该方法不仅能够降低K值即选择多少个邻居对最近邻算法的影响,减少其内存开销,而且能够有效缩短忠诚度分类的时间以及提高忠诚度分类的准确度。
Abstract:
In order to improve the accuracy and efficiency of loyalty prediction,this paper,combining K nearest neighbor(KNN)with Bayesian algorithm as a classification prediction method,proposes a loyalty prediction method based on improved nearest neighbor.The method first takes high loyal customers and low loyal customers as the same category of loyal customers.The method classifies the data set with Bayesian algorithms to obtain non-loyal customers and loyal customers.It takes loyal customers as test data set of the following KNN algorithm and classifies them to obtain high loyal customers,low loyal customers and non-loyal customers.The experimental results show that this method can not only reduce the impact of the K value on the nearest neighbor algorithm and reduce its memory overhead,but also can effectively shorten the time of loyalty classification and improve the accuracy of the classification accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-06-29 修回日期:2016-09-15基金项目:国家重点研发计划政府间国际科技创新合作重点专项(S2016G9070); 江苏省重大研发计划社会发展项目(BE2017739); 江苏省重大研发计划产业前瞻项目(BE2017100); 中央高校基本科研业务费专项资金(30916015104); 赛尔下一代互联网创新项目(NGII20160122); 中兴通讯产学研合作论坛合作项目(2016ZTE04-11)
作者简介:朱虹(1993-),女,硕士生,主要研究方向:信息安全,数据挖掘,E-mail:18251953319@163.com; 通讯作者:李千目(1979-),男,博士,教授,主要研究方向:信息安全,E-mail:liqianmu@126.com。
引文格式:朱虹,李千目,戚.一种基于改进最近邻算法的忠诚度预测方法[J].南京理工大学学报,2017,41(4):448-453.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2017-08-31