摘要
为了更及时、清晰地发现洗钱的踪迹 ,减少反洗钱的工作量 ,基于增量层次算法聚类以及划分算法聚类的思想 ,将中心点的思想应用到BIRCH算法中聚类特征 (CF)的计算 ,用核心树代替CF树 ,可以更加适用于类似金融数据这样数据类型复杂 ,含有“噪音”
To find the trace of money laundering and to decrease the workload of detecting money laundering, the idea of center object was applied to the computing of “Clustering Feature (CF)” in BIRCH algorithm based on the increment arrangement clustering algorithm and district clustering algorithm. CF tree was substituted for the “Core-Tree”, which will be more suitable to the large volume data set like financial data, which has various data and “noise”.
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2004年第11期85-87,共3页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
关键词
反洗钱
增量聚类
中心点
核心树
detecting nomey laundering
increment clustering
center object
Core-Tree
