运用模糊集技术处理商业银行中的非精确数据，对个人客户进行聚类分析，并通过实例分析了实现银行客户分类的整个过程。
A method of dealing with non-accurate data in commercial bank based on fuzzy set is presented, and clustering analysis of personal customer is introduced.
我们目前的一个银行客户将InformationFrameWork作为扩展检查清单使用，用于对超过300个大型机应用程序中的数据和应用程序资产进行分类。
A current banking client USES the Information FrameWork to act as an extended checklist for the classifying data and application assets in their 300 + mainframe applications.
基于非平衡数据集的支持向量域分类模型，提出了一种银行客户个人信用预测方法。
A new predication method of customer credit of Banks is proposed based on the support vector domain classification model of non-balance data set.
由于银行客户数据库中存在大量模糊和噪声数据，应用模糊集技术进行聚类分析，可以提高客户分类的效率和精确度。
There are lots of fuzzy and noisy data in bank database, by application of fuzzy set improve the efficiency and accuracy of customer classification.
由于银行客户数据库中存在大量模糊和噪声数据，应用模糊集技术进行聚类分析，可以提高客户分类的效率和精确度。
There are lots of fuzzy and noisy data in bank database, by application of fuzzy set improve the efficiency and accuracy of customer classification.
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