英文标题：
《Clustering Approaches for Financial Data Analysis: a Survey》
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作者：
Fan Cai, Nhien-An Le-Khac, Tahar Kechadi
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最新提交年份：
2016
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英文摘要：
  Nowadays, financial data analysis is becoming increasingly important in the business market. As companies collect more and more data from daily operations, they expect to extract useful knowledge from existing collected data to help make reasonable decisions for new customer requests, e.g. user credit category, confidence of expected return, etc. Banking and financial institutes have applied different data mining techniques to enhance their business performance. Among these techniques, clustering has been considered as a significant method to capture the natural structure of data. However, there are not many studies on clustering approaches for financial data analysis. In this paper, we evaluate different clustering algorithms for analysing different financial datasets varied from time series to transactions. We also discuss the advantages and disadvantages of each method to enhance the understanding of inner structure of financial datasets as well as the capability of each clustering method in this context.
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中文摘要：
如今，财务数据分析在商业市场中变得越来越重要。随着公司从日常运营中收集越来越多的数据，他们希望从现有收集的数据中提取有用的知识，以帮助对新客户的请求做出合理的决策，例如用户信用类别、预期回报的信心等。银行和金融机构已应用不同的数据挖掘技术来提高其业务绩效。在这些技术中，聚类被认为是捕获数据自然结构的重要方法。然而，对于财务数据聚类分析方法的研究并不多。在本文中，我们评估了用于分析不同金融数据集（从时间序列到交易）的不同聚类算法。我们还讨论了每种方法的优缺点，以增强对金融数据集内部结构的理解，以及在这种情况下每种聚类方法的能力。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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