摘要
随着数据量的迅速增加,如何对大规模数据进行有效的聚类成为挑战性的研究课题。面向大数据的聚类算法对传统金融行业的股票投资分析、互联网金融行业中的客户细分等金融应用领域具有重要价值。对已有的大数据聚类算法进行了详细划分,并比较了每种聚类算法的优缺点,进一步总结了已有研究存在的问题,最后对未来的研究方向进行了展望。
With the rapid increase of data size,it is a challenge to cluster the large scale data.Clustering algorithms for big data are very important for the stock investment analysis in the traditional finance field,customer segmentation in Internet finance field and so on.Firstly,the existing clustering algorithms for big data were divided,and then the advantages and disadvantages of each type were compared.After that,the problems of the existing researches were summarized.Finally,the future research directions were given.
出处
《计算机科学》
CSCD
北大核心
2016年第S1期380-383,共4页
Computer Science
基金
北京高等学校青年英才计划项目(YETP0988)资助
关键词
大数据
聚类算法
股票投资分析
客户细分
Big data
Clustering algorithms
Stock investment analysis
Customer segmentation
