基于社会网络可视化分析的数据挖掘(英文)

Networked Data Mining Based on Social Network Visualizations

把社会等复杂系统看作网络的思想由来已久.利用社会网络分析的方法,能够对各种社会关系进行精确的量化表征和分析。从而揭示其结构,对一系列当代社会的现象进行更加深入而具体的解释.结合社会网络可视化分析和数据挖掘的理论与方法,引入相关的地理信息,对包含1980-2002年间世界范围内1417例恐怖袭击事件的数据库进行数据分析,以这些恐怖袭击事件各要素节点之间关系作为基本分析单位,对恐怖组织之间的活动模式和发展特点等内在规律进行挖掘与解释,得出有意义的结果.提出的方法可以有效地推广应用于蛋白质结构分析、生物基因分析以及各类社会问题的分析过程.

Studies in social network theory focus on characterizing complex social relationships by firstly mapping and visualizing them into a graph, and then subsequently identifying the corresponding graph properties. This paper provides an integrated approach, which combines social network analysis and data mining theory with the necessary geographical attributes to analyze 1417 instances of terrorism that occurred world wide during the period 1980-2002. The study reveals interesting patterns on the evolution of these terrorist organizations over two decades. The proposed method can be easily generalized to be applied to other types of large-scale networked datasets, such as micro-array data, and genomic networked data, etc.