摘要
反洗钱中的一个重要问题是预测可疑账户未来可能发生的交易。马尔科夫模型在股票、商品价格、市场占有率等经济领域的预测中具有广泛的应用,但单一的马尔科夫模型的预测准确性有待提高。提出一种结合数据挖掘中聚类、关联规则和低序马尔科夫模型的混合马尔科夫模型,并在模型的建立过程中基于置信度进行剪枝以降低时间复杂度,最后将该模型用于预测反洗钱领域中账户之间的交易。实验表明,该模型具有较高的预测准确性,并在预测准确性和时间复杂度两者之间取得了较好的平衡。
An important problem in anti-money laundering is to predict the possible transactions conducted by suspicious accounts.Markov model has a wide range of applications in economic predictions such as stock,commodity prices,market share and so on.But the prediction accuracy of the single markov model remains to be improved.A hybrid Mar-kov model jointing with clustering,association rule and low order Markov model was proposed.In the process of constructing the model,the confidence-based pruning was conducted to reduce the time complexity.Finally,the model was used to predict the transactions among accounts in anti-money laundering.The experimental results show that this mo-del has high prediction accuracy and is a good tradeoff between the prediction accuracy and the time complexity.
出处
《计算机科学》
CSCD
北大核心
2011年第7期170-174,共5页
Computer Science
基金
国家自然科学基金项目(70771043)
国家自然科学基金项目(60873225)
国家863计划项目(2007AA01Z403)资助
关键词
混合马尔科夫模型
预测
聚类
关联规则
反洗钱
Hybrid Markov model
Prediction
Clustering
Association rule
Anti-money laundering
