基于云模型的关联规则挖掘及其在信用卡营销中的应用

Yan-li Zhu, Yu-Fen Wang, Shun-Ping Wang, Xiao-juan Guo
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引用次数: 3

摘要

关联规则挖掘是KDD应用中的一个重要问题。本文首先利用云模型对属性值进行动态划分,克服了以往基于经验划分概念的缺点,然后利用改进的Apriori算法探索了云模型在信用卡数据库关联规则挖掘中的应用。实验结果表明,该方法具有控制不确定性的有效性和灵活性。通过对云关联规则的分析,可以为商业银行实施个性化营销提供有价值的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining Association Rules Based on Cloud Model and Application in Credit Card Marketing
Mining association rules is an important issue in KDD applications. In this paper, we first use the cloud model to dynamically divide attribute value to overcome the shortcoming that the concept was partitioned by experience, and then explore the application of cloud models in mining association rules from credit card database by the improved Apriori algorithm. The result of experiment shows that the method is effective and flexible in holding uncertainties. By analyzing cloud association rules, valuable advice can be provided for the commercial banks to implement personalized marketing.
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