[1]汪永旗,王惠娇.旅游大数据的MapReduce客户细分应用[J].华侨大学学报(自然科学版),2015,36(3):292-296.[doi:10.11830/ISSN.1000-5013.2015.03.0292]
　WANG Yong-qi,Wang Hui-jiao.Application of Tourist Segmentation Based on MapReduce under Big Data of Tourism[J].Journal of Huaqiao University(Natural Science),2015,36(3):292-296.[doi:10.11830/ISSN.1000-5013.2015.03.0292]
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旅游大数据的MapReduce客户细分应用()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第36卷
期数:
2015年第3期
页码:
292-296
栏目:
出版日期:
2015-05-20

文章信息/Info

Title:
Application of Tourist Segmentation Based on MapReduce under Big Data of Tourism
文章编号:
1000-5013(2015)03-0292-05
作者:
汪永旗1; 2;  王惠娇3
1. 杭州电子科技大学 自动化学院, 浙江 杭州 310018;2. 浙江旅游职业学院 旅行社管理系, 浙江 杭州 311231; 3. 浙江理工大学 机械与自动控制学院, 浙江 杭州 310018
Author(s):
WANG Yong-qi1; 2;  Wang Hui-jiao3
1. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; 2. Department of Travel Agency Management, Tourism College of Zhejiang, Hangzhou 311231, China; 3. School of Mechanical Engineering and Automation, Zhejiang Sci-Tech University,
关键词:
旅游大数据;  MapReduce模型;  聚类;  客户细分
Keywords:
tourism big data;  MapReduce model;  clustering;  customer segmentation
分类号:
TP39
DOI:
10.11830/ISSN.1000-5013.2015.03.0292
文献标志码:
A
摘要:
分析K-means聚类算法和Hadoop云平台的特点,对聚类算法进行改进,给出算法的MapReduce实现.通过加速比实验和旅游数据细分实验,验证了算法的有效性和高可扩展性.针对旅游大数据的特点,构建了多指标的RFM扩展模型,通过文中算法聚类,得到与预期相近的聚类结果.实验结果表明:文中算法具有较高的实用价值.
Abstract:
First, the characteristic of K-means clustering algorithm and Hadoop cloud platform is analyzed in this paper, the improvement of K-means clustering algorithm and its implementation of MapReduce are given. Then, the experiments of speedup and tourist segmentation are given to illustrate the effectiveness and the high scalability of the proposed method. Finally, according to the characteristics of tourism big data, a multi index RFM model is built, the clustering results which are expected indicate that the algorithm is highly practical.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2015-05-07
通信作者: 汪永旗(1973-),男,讲师,博士研究生,主要从事旅游电子商务和数据挖掘的研究.E-mail:68279983@qq.com.
基金项目: 浙江省自然科学基金资助项目(Y14D060013); 浙江省教育厅校企合作项目(FW2013031)
更新日期/Last Update: 2015-05-20