摘要
在互联网大环境下,人们的生活方式和生活水平发生了巨大的变化,而随着各大网站商城、社交平台而产生的大量网络客户评论里,蕴含着丰富的信息。探索这些评论中针对商品或服务的消费者行为特点,帮助制造商和服务商挖掘有用的信息,从而改进自身产品和改善相关的服务,提高企业核心竞争力。文中采用基于聚类分析中无指导学习型K-means算法,对某一电商网站上真实客户评论数据集进行分析,在客户属性数据中挖掘出有趣的信息来,并根据这些有趣的结论进一步调整企业在电商模式下的产品运营管理。
People＇s life style and living standard have taken great changes because of Internet. Meanwhile, the reviews of commodities on the web conclude lots of valuable information. Mining interesting things to help analyze customers＇ behaves and that can assist manufacturers and service providers with improving products and services. Based on the wide application of clustering analysis in data mining, this paper uses K-means to find interesting characteristic from customer attributes and products categories. And according to these results, commercial enterprises can further adjust products operation management on Internet.
出处
《物流工程与管理》
2014年第4期86-89,115,共5页
Logistics Engineering and Management
基金
国家社科基金
基金号:13BTJ005
中央高校基金
基金号:2013XZD01
广东省产学研基金
基金号:2012B091100309
和2012B040500010
