【摘要】 孤立点发现是数据挖掘活动的重要组成部分,被广泛应用于电子贸易、信用卡等领域的欺诈检测。由于优良的拓扑结构保持和概率分布保持特性,SOM(Self-Organizing Maps)可作为一种有效的降维工具供分析人员获取隐藏于数据中的分布结构信息。在分析了当前基于距离的孤立点发现的基础上,提出了一种基于SOM的孤立点发现与预测新途径,具有可扩展性、可预测性、交互性、简明性等特征。实验结果表明,基于SOM的孤立点发现与预测是有效的。更多还原

【Abstract】 Outlier detection is an integral part of data mining and is critical important to some areas such as monitoring of criminal activities in electronic commerce,credit card fraud,etc. Due to the topological structure and probabilistic distribution preserving nature,SOM (Self-Organizing Maps)has been used as a tool for mapping high-dimensional data into a two dimensional feature map and gaining some idea of the structure of the data by observing the map. Based on the analysis of the existing distance-based outlier detection algorithms,a SOM based approach to detect and predict outliers is proposed,which has an obvious superiority in scalability,predictability,interactiveness,conciseness. Experimental results on real database show that the SOM based outlier detection and prediction is effective.更多还原